Proceedings of the2nd International Conference on Bio Signals, Images and Instrumentation, (ICBSII -...

Proceedings of the

2Nd International conference on

Bio signals, Images and Instrumentation

Centre for Healthcare Technologies

Department of Biomedical Engineering

SSN College of Engineering

19th

- 21st March 2015

Editorial Board

Chief Editor : Dr. A. Kavitha

Co-editors : D. Kanchana

R. Anandha Sree

2nd International Conference on Bio Signals, Images and Instrumentation, (ICBSII - 2015).

ii

Contents

From the President's desk

Mrs. Kala Vijayakumar

vi

From the Principal's desk

Dr. S. Salivahanan

vii

Convener's Message

Dr. A. Kavitha

viii

Message from the Conference Secretary

Dr. L. Suganthi

ix

Conference Organizing Committee

x

Organizing Team

xi

Technical Advisory Committee

xi

Student Organizing Team

xii

Keynote Speakers

Mr. Kannan Thiruvengadam

II

Dr. Sudhir Ganeshan

Biomechanics of Spine and Spinal Instrumentation

III

Dr. Syrpailyne Wankhar

Physiological Measurement and issues of Signal Processing

V

Dr. Vijayalakshmi

Advances in imaging methods applied for genetic diagnosis

VII

Dr. Prashant Kumar Srivastava

Mathematical Modeling of HIV Infection: in vivo

IX

Dr. Brajesh K Kunwar

Advances in cardiac interventional Techniques in management of structural heart

disease

XI


iii

Research Papers

Sl.

No

Paper ID

Title Page No.

1 ICBSII-BMI013

Characterization of Electrohysterograms using Hurst

Exponents Tharini M, Kamalanand K

1

2 ICBSII-SP001

Finger Movement Recognition using Neural Network Angana Saikia, Nitin Sahai

6

3 ICBSII-BMI011

Development of A System for Analysis of Remote

Auscultation Signal for Management of Chronic Asthma

Cases Bony George, Ajith K.G., George Varkey, Rajesh M

9

4 ICBSII-IP017

FPGA Implementation of Distributed Canny Edge

Detector Algorithm Dhinakaran M, C. M. Sujatha

15

5 ICBSII-IP019

VLSI Implementation for Imaging Based Classification

of Neurodegenerative Diseases- A proposal Neha Gopal. N, T. Christy Bobby

21

6 ICBSII-BMI003

Real Time pH Measurement for Monitoring Effluent

Dialysate in an Artificial Kidney Hemalatha R. J, Shaliya B, Saranya B.

23

7 ICBSII-IP001

Spatial Domain Filters in Preprocessing of Optical

Coherence Tomography Arati Sinha, Jintu Das, K.Venkataraman

27

8 ICBSII-IP004

Impulse Noise Reduction from Mammogram Images

using Novel KT Filters R. Subash Chandra Boss, K. Thangavel

34

9 ICBSII-IP002

Detection and Extraction of Blood Vessel of Retinal

Images in Diabetic Retinopathy using Filters Subhra Pattnaik, Asit Subudhi, Sunita Sarangi, Sukanta Sabut

40

10 ICBSII-IP010

A Novel Earlier Stage Detection Scheme for Glaucoma

Disease using Automated Classification Method M.Kirthikavathi , M.Mahendraselvi, S.Veluchamy

45


iv

11 ICBSII-IP016

Feature Extraction using SIFT for Magnetic Resonance

Image Sathiyaseelan. J, Mr. C. John Moses

51

12 ICBSII-BMI001

Simulation of Critical Home Health Monitoring & Drug

Delivery System N. Abhinaya, G. Hari Krishnan, R. J. Hemalatha, G. Umashankar

55

13 ICBSII-BMI012

Design And Development of Diagnostic Tool for Strain

Injuries of Finger Joints using Flex Sensor Balakumaran.V, Bhuvaneshwari.S, Nithyaa.A.N, Premkumar.R

59

14 ICBSII-GE001

Cluster Analysis of Gene Expression Data using

Optimization Techniques: A Survey B. Nithya, R. Rathipriya

63

15 ICBSII-BMI004

Automated Ambu Bag System used in Emergency

Situation Archana J, Mary Helta Diasy

68

16 ICBSII-SP003

Discrimination of SEMG Signals Based on Temporal and

Spectral Approach for Frailty Analysis Vidya K V, E. Priya

72

17 ICBSII-BMI015

Model Identification and Controller Design of a

Perfusion System During an ECMO Support M.Dhinakaran , Dr S.Abraham Lincon, P.Praveen Kumar

80

18 ICBSII-IP009

A Technique to Implement Bimodal Biometrics, for

Preventing Infant Mix-Ups using Raspberry PI S.Sivaranjani, Dr. S.Sumathi

86

19 ICBSII-SP006

Feature Extraction and Comparison of Motor Activities

for Cursor Control Sasweta Pattnaik, Manasa Dash, Sukant Sabut

93

20 ICBSII-BMI014

Non-Invasive Device for Measurement of Glucose and

Haemoglobin in Blood K.M.Nivetha, K.Pavithra, N.Indhujha, D.Arulkumar

98

21 ICBSII-BMI008

Physiotherapy for Rehabilitation using Motion Sensor Sheeba Abraham, Vamsi Krishna

102

22 ICBSII-IP008

Palm Print Based Security System for Lockers Angelena Mathias, S. Caroline

107


v

Dedicated to

all Staff and Students of

the Department of

Biomedical Engineering


vi

From the President's desk

The numerous achievements of SSN College of Engineering are a direct result of the institution’s strong

backbone of faculty and students. Academic excellence apart, the institution also strives to instill in its students, traits such as critical thinking, work ethic, and the sense of responsibility towards society. Every

aspect of SSN, from the campus to the libraries, is aimed at ensuring the holistic development of its

students. The Research Centre at SSN and its success is a testimony to the emphasis laid on research and development.

Biomedical Engineering (BME) as a discipline is truly multi-disciplinary and offers varied research

avenues. We also observe that in this day and age, it has become imperative to stay abreast with all the latest developments in technology. The importance is two-fold when the technology is linked with

healthcare.

The department collaborates with Centre for Healthcare Technology (CHT), a multi-disciplinary R&D

centre, which is an initiative for bringing together technologists, engineers, doctors, and healthcare

professionals, industry, and government to develop healthcare technologies for the country.

The mission of the BME Department is to pursue excellence in biomedical engineering education,

research, and innovation; creating and imparting knowledge for improving society, human health, and

healthcare. Taking cognizance of this fact, the BME department’s effort to host an International Conference certainly deserves appreciation.

On behalf of the SSN CE Management, I congratulate the BME department and also wish this initiative all success.


Chief Patron, ICBSII – 2015.


President, SSN Institutions


vii

From the Principal's desk

Dr. S. Salivahanan

Principal, SSN College of Engineering

The SSN Group of Institutions has emerged as one of the best private educational institutions in

the country in terms of academics, infrastructure, and extra-curricular achievements. The main

aim of SSN is to make a positive difference to the society through education.

Biomedical engineering is indeed a holistic discipline and successfully integrates engineering

technology with healthcare. It serves as a home to award-winning faculty, exceptional students,

numerous research centres, and laboratories engaged in an array of interdisciplinary biomedical

activities. The department’s inclination towards research has to be appreciated and the fact that

the enthusiasm is prevalent in students and teachers alike is commendable. The department has

also signed various memorandums with many companies and medical colleges to facilitate

students in the path of their research.

The department collaborates with Centre for Healthcare Technology (CHT), a multi-disciplinary

R&D centre, which is an initiative in bringing together the experts of various fields, to develop

healthcare technologies for mankind.

The Biomedical Engineering Department continues its trail of success by organizing the

International Conference on Bio Signals, Images and Instrumentation-ICBSII in a manner

befitting the stream. I congratulate the entire team at BME for planning this to perfection and

wish them all the very best!

Dr. S. Salivahanan Patron, ICBSII – 2015.


viii

Convener's Message

It gives me great pleasure to convene the second International Conference on Bio Signals,

Images, and Instrumentation-ICBSII, 2015. The department is effectively affirming its part in

imparting knowledge to young researchers with great thirst for knowledge and scientific

curiosity by organizing this conference.

The Biomedical Engineering fraternity is one which is oblivious to the boundaries of institutions,

states, countries, etc. This is understandable, given that the field of study is intertwined with

medicine and healthcare. The outcomes of research assume a whole new level of importance and

significance. The discipline also sees growth on a regular basis, an aspect which is exciting while

being challenging. We have strengths in numerous research areas, boast a wealth of research

resources and facilities. Our BME department is known for its highly quantitative approach to

biomedical science, with liberal application of engineering principles and physical sciences.

The department joins hand with Centre for Healthcare Technologies (CHT), a multi-disciplinary

R&D centre of SSN, which focuses on developing healthcare technologies. CHT collaborates

with leading medical institutions and healthcare industries in developing R&D solutions, joint

development of technology products, technology assessment, and evaluation by standardizing

diagnostic procedures, building rural clinics, and developing streamlined health IT systems. CHT

also works to develop human resources in healthcare technology in the country through

internships and projects.

The International Conference on Bio Signals, Images, and Instrumentation (ICBSII), organized

by SSN College of Engineering, is a core biomedical academic event aimed at discussing latest

trends in bio signal and image processing research and bridging the industry institution gap

through debates on bioinstrumentation. The conference provides a platform for academicians,

students, clinicians and researchers to observe, discuss and showcase advancements in

biomedical research. They say teamwork divides the tasks and multiplies the success. I hope that

the success of the conference sets a new benchmark in academia.

Dr. A. Kavitha

Chair Person, ICBSII – 2015.

Dr. A. Kavitha

Head, Department of Bio-Medical Engineering,



ix

Message from the Conference Secretary

The international conference on Bio Signals Images and Instrumentation (ICBSII 2015) is being

organized to share and enhance the knowledge of young biomedical engineers, researchers and

faculty of various institutions in the field of biomedical signal, image and instrumentation. This

conference aims to stimulate the students for transforming their theoretical knowledge into

biomedical products which will help the society. This conference creates a bridge between the

doctors and engineers. Since doctors are handling the biomedical instruments, signal and image

processing systems which is proposed and validated by engineers, this conference brings out real

time research problems addressed by the doctors in their fields of specialization and young

engineers can work towards those research problem. The conference also creates an environment

for the scientists, researchers and students from all over the world to share their ideas,

experiences and results of their scientific research work and shows the way for research

collaboration.

I take immense pleasure to express my sincere and deep sense of gratitude to the Management of

SSN College of Engineering, Ms. Kala Vijayakumar, President, SSN Institutions and Dr.

Salivahanan, Principal, SSN College of Engineering for granting a wonderful opportunity to

organize this conference and for helping us to promote our research. I would like to give my

special thanks to delegates Mr. Kannan Thiruvengadam, Director, Platinum,Aurea Inc., Austin,

TX, USA, for inaugurating this conference, Dr. Sudhir ganeshan, Gangaram Hospital, Delhi, Dr.

Syrpailyne Wankhar, CMC, Vellore, Dr. Brajesh Kumar Kunwar, care hospital, Surat,

Dr.Prashant Srivastava, IIT Patna, Dr. Vijayalakshmi, Sri Ramachandra University,Chennai, for

their valuable speech to motivate us towards the quality research. I would like to thank all the

faculty, staff and students of Biomedical department for making this function grand success.

Dr.L.Suganthi

Secretary, ICBSII – 2015.

Dr. L. Suganthi,

Associate Professor, Department of Bio-Medical Engineering,



x

Conference Organizing Committee

Chief Patron:

Mrs. Kala Vijayakumar,

President

SSN Institutions

Patron:

Dr. S. Salivahanan,

Principal,

SSN College of Engineering.

Chairperson:

Dr. A. Kavitha,

Head, Department of Biomedical Engineering,

SSN College of Engineering, Chennai.

Secretary:

Dr. L. Suganthi

Ms. D. Kanchana

Treasurers:

Dr. R. Subashini

Ms. Delpha J

Mr. R.Yuvaraj

Organizing Team:

Dr. S. Pravin Kumar

Dr. V. Mahesh

Dr. Guruprakash subbiahdoss

Dr. Mallika Jainu

Mr. R. Sivaramakrishnan


xi

Mrs. B. Geethanjali

Dr. Sachin Gaurishankar Sarate

Mrs. M. Dhanalakshmi

Ms. R. Nithya

Technical Advisory Committee

Dr. Neeradha Chandramohan, NIEPMD

Dr. Sudhir Ganesh, Ganga Ram Hospital, Delhi

Dr. S. Ramakirishna, IITM

Dr. S. Rajendran, Sri Ramachandra University

Dr. Ganesh Venkatraman, Sri Ramachandra University

Student Organizing Team

Food Committee

A. Siva., Final Year

Nagasai., Final Year

Vijaya Balaji S., Final Year

Hall Arrangement Committee

Muthu Vijay., Third Year

Deepak., Third Year

Muthumeenakshi ., Third Year

Transportation Committee

Hemanath N., Final Year

Prasanth P., Final Year

Cultural Committee

Hemavardhini S., Final Year


xii

Aishwariya., Final Year

Photography Committee

Raj Kumar., Final Year

Saravana Prakash., Third Year

Registration Committee

Siva A., Final Year

Kulddep Surana., Final Year

Vardhini., Final Year

Guest House/Accommodation Committee

R. Naren., Final Year

V. Naren., Final Year

Conference Proceedings Committee

Angel Jenifer.P, M.E– First Year

Bhuvaneshwari.B, M.E– First Year

Deepthi.K.S, M.E– First Year

Diwakar.M, M.E– First Year

Guhan Seshadri.N.P, M.E– First Year

Meenachi.P, M.E– First Year

Murali.S, M.E– First Year

Sriranjani.S, M.E– First Year

Vaishali.R, Final Year

Yuvadharshini.I, Final Year

Sarah Rajitha Thilagam.V, Final Year

Prasanna Bharati.R, Final Year


I

2nd

International Conference on Bio Signals,

Images and Instrumentation

ICBSII - 2015

Keynote Talks


II

Chief Guest

Mr. Kannan Thiruvengadam,

Director, Platinum

Aurea Inc., Austin, TX, USA

Kannan has been a technology person right from school. He could speak the language of the

machine, and took great joy in programming in both high-level and low-level languages. He studied

algorithms and data structures, and enjoyed choosing the right method and structure depending on

the nature and size of the problem. He’s won several awards in college. He went to NIT Trichy.

Alongside, he has gone out and taught village school children languages he knew, through the

Computer Society of India and for raising funds for college events.

He rejected his campus interview offers and stayed back in his college, working for the computer

department developing learning utilities for his juniors. At that time, even a network file transfer tool

was a novelty. After a year of such service, from the hot and chaotic climate of Trichy, he went to

the cold and calm of Western Canada, close to the Rockies. He completed his Masters in Computing

Science at the University of Alberta, and moved to the United States for research and work in

multimedia.

He has held a variety of industrial positions since then, covering a myriad of topics, and in various

industries, including health-care, travel and entertainment, telecommunications, retail, insurance

(Swiss Reinsurance) and finance (Bank of New York, Bank of America). His work took him to

many parts of the world, including Europe and East Asia, which means he has worked with people

of various cultural and linguistic backgrounds, and on problems that are unique to their contexts. His

technical background includes business process management, middleware, communication security

and operation of Production systems. Lately, Kannan has taken an interest in the application of

technology in what he considers the most critical issue of our times: climate change and possible

applications of biotechnology there.


III

Speaker Profile:

Dr Sudhir Ganeshan is a Consultant Orthopaedic and Spine Surgeon registered with

Indian Medical Council and Tamil Nadu Medical Council. His academic accolades include a

FNB (Fellowship in National Board) in Spine Surgery from Sir Ganga Ram Hospital, New Delhi

The only certified course for Spine Surgery in the country under National Board of

Examinations, Ministry of Health and Family Welfare, MNAMS (Membership of National

Academy of Medical Sciences) in 2012, DNB in Orthopedics 2008 to 2011 - Ganga Hospital,

Coimbatore under National Board of Examinations and he is currently pursuing a 2 year

Fellowship in EULAR (European League against Rheumatism). He was also awarded with the

Best Outgoing Spine Fellow of Ortho Spine Department, Sir Gangaram Hospital. He is also an

integral member of the Delhi Spine Society and Coimbatore Orthopedic Society and has

performed and assisted almost all types Spine surgeries for trauma, deformity correction,

degenerative disorders, inflammatory disorders, tumours, infections, including minimally

invasive surgeries and Endoscopic spine surgeries.

Dr. Sudhir Ganeshan

Consultant Orthopaedic and Spine

Surgeon


IV

Biomechanics of Spine and Spinal Instrumentation

-Dr. Sudhir Ganeshan.

Abstract:

The spine is a complex mechanical structure complete with levers (vertebrae), pivots (facets

and discs), passive restraints (ligaments), and actuators (muscles). Instrumentation of spine for

various pathologies including deformity corrections requires a thorough understanding of the

biomechanics of Spine. Moreover, designing implants and instrumentation for spine fixations

warrants an adequate insight into the biomechanics of normal and abnormal states. The field

of modern biomechanics has deep historical roots from the ancient Egyptians, who

documented the earliest accounts of spinal injury. Building on this foundation, the modern

explosion of spinal instrumentation introduced the concept of internal fixation for spinal

stabilization, further advancing the understanding of the mechanics of musculoskeletal

motion. The spinal instrumentation has revolutionized since the advent of pedicle screw based

system. Various techniques and instrumentation systems like fenestrated screw, minimally

invasive instrumentation are on the rise with improvisation of the biomechanical principles of

implants. In spite of such advances in this field, all the techniques have some cons which

eventually results in poor outcome for the patients. This may be due to poor understanding of

the normal biomechanics of the spine or of the implants or of both. Clinical biomechanics

requires the assessment of 3 key questions: 1) how do the components of the implant connect

together, 2) how does the implant connect to the spine, and 3) how does the construct function

biomechanically. The ability to apply these basic biomechanical principles in the clinical

arena is not only important for the Spine surgeons for decision making in treating the spinal

disorders but also for the Biomedical engineers for development of new instrumentation

systems which will overcome the existing disadvantages ultimately resulting in a better

clinical outcome for the patients.


V

Speaker Profile:

Dr. Syrpailyne Wankhar has received the Ph.D. degree in Bioengineering from CMC Vellore,

M.Tech degree in Biomedical Engineering from IIT, Bombay and B.E. degree in Electrical

Engineering from MMMEC, Gorakhpur. Her professional experience include working for a year

in the department of Physiology, AIIMS, New Delhi, and research assistant and teaching

assistant at CMC, Vellore. She has been one the faculty of CMC, Vellore since 2012, currently

as a Lecturer in the Department of Bioengineering. In the last 5 years she has collaborated with

the departments of Orthopedics, Surgery, Physiology, Psychiatry in CMC Vellore for research

projects like Assessment of knee function and proprioception following anterior cruciate

ligament repair, Development of an in-house NIR imaging for lymph node detection in breast

cancer, Reflex modulation before and after exercise. In addition, she holds a patent for a

magnetic stimulator design for high frequency stimulation. Some of her works have been

published in the Journal of Bone and Joint Surgery, Journal of Medical Devices.

Dr. Syrpailyne Wankhar,

Department of Bioengineering,

CMC Vellore.


VI

Recording and Analysis of Biopotential Signals

-Dr. Syrpailyne Wankhar.

Abstract:

All measurement system requires prior information about the quantity being measured in order to

be able to distinguish signal of interest from any unwanted signal. It is also important that the

measuring system does not alter the system being measured. Similarly, for physiological

measurement we need to understand the physiological system under investigation so that the

signal of interest can be recorded accurately and any interfering signal can be detected and

removed. The purpose of a physiological measurement is to understand the underlying system.

In this lecture, I will discuss some aspects of the physiological measurement and issues of signal

processing. As an engineer more than a mere theoretical explanation, a demonstration of the

physiological measurement will be more meaningful in understanding the underlying system. I

will present such a demonstration in this lecture. Discussion of systems characteristics, dynamic

response, physiological interpretation will be based on the demonstration. For example, the

temporal and spatial characteristics of ECG can provide useful insight about the location or

origin of a problem in the cardiovascular system. This aspects will also be brought out in the

demonstration and subsequent discussion during the lecture.


VII

Speaker Profile:

Dr.Vijayalakshmi has completed Ph.D at Department of Human Genetics, Sri Ramachandra

University. She has participated in over 20 workshops including International Conference on

Radiation Biology, Nanotechnology, Imaging and Stem Cell Research in Radiation Oncology

(ICRB-NISRRO), Prenatal & Postnatal Diagnosis of Genetic Disorders using Molecular

methods(conducted by AIIMS, New Delhi).She has published articles in International Journal of

Human Genetics.In addition,she has worked as an investigator in projects funded by DRDO-

INMAS.She has received Dr Todla Ekambaram Prize for General Proficiency in Botany and

award for having rendered Ten years of Continuous Service at the Sri Ramachandra University.

Dr. Vijayalakshmi J,

Assistant Professor,

Department of Human Genetics,

Sri Ramachandra University,

Chennai


VIII

Advances in imaging methods applied for genetic diagnosis

-Dr. Vijayalakshmi J

Abstract:

Cytogenetic analysis is the study involving structure and function of chromosomes which is an

important tool employed in the diagnosis of genetic disorders. The chromosomes are prepared

either from chorionic villi, amniotic fluid, cord blood, bone marrow or peripheral blood

lymphocytes in patients presenting with abnormal clinical findings. The preparation of

chromosomes involves cell culture, metaphase arrest, hypotonic treatment, cell fixation, slide

preparation and use of banding methods. The analysis is done by taking photomicrographs of the

metaphase spreads, from which each chromosome is cut, paired and the arrangement of the

chromosomes (karyotype) is prepared manually. This whole procedure is time consuming and

laborious. The increased need of the cytogenetic laboratories and availability of improved

computer capabilities for image processing, the Image Analyzing System, with appropriate

software, are being used for cytogenetic analysis. The system includes a high resolution research

microscope with or without an automatic metaphase scanning stage, charged couple device

(CCD) camera, computer with special software for image capturing, chromosome counting, and

automatic karyotyping. At its present stage of development, the system will perform a complete

analysis of well spread, homogeneously stained metaphase chromosomes. The system

incorporates a metaphase finder to detect cells of good quality. Chromosomes in a selected cell

are segmented by their grey level using a locally determined threshold. An axis and a

centromere are automatically assigned to each chromosome. Software is used to classify the

chromosomes into seven groups based on number, size and position of the centromere. A grey-

level karyogram can be displayed for interpretation with an analysis time is about two minutes

per cell. This system has made storing and retrieval of data very easy. Therefore, the output

exquisite analytical report is conducted with the soft ware may increase the rapidity in reporting

genetic test.


IX

Speaker Profile:

Dr.Prashant Kumar Srivastava has worked as Assiatant Professor at BITS, Pilani and Post

Doctoral Fellow at IIT Kanpur. He has presented papers in Mathematical Modeling in Ecology

and Epidemiology, Applications of Differential Equations in Biology in various journals. He has

received fellowships such as the JRF,SRF and NBHM Post Doctoral Fellowship that is given by

Department of Atomic Energy. He has also received awards such as IMS Prize for Best Paper in

Biomathematics by Indian Mathematical Society , Best Tutor award from Director IIT Kanpur

and Best Teacher Awards of IIT Patna .In addition, he is a member of societies such as Society

for Mathematical Biology (SMB), Indian Mathematical Society (IMS) and Indian Academy for

Mathematical Modelling and Simulation (IAMMS).

Dr. Prashant Kumar Srivastava

Asst. Professor,

IIT-Kanpur


X

Mathematical Modeling of HIV Infection: in vivo

-Dr. Prashant Kumar Srivastava

Abstract:

AIDS, which begins with deterioration of the immune system, is caused by infection with

Human Immunodeficiency Virus (HIV). It is one of the most fatal diseases and is causing huge

loss to social and economical development of many countries, especially in developing countries.

HIV primarily attacks CD4+ T cells that cause the destruction and depletion of these cells

leading to compromised immune system and inability to fight with opportunistic infections. In

this talk we shall give an overview of mathematical modeling of the HIV and immune system

interaction. We introduced a simple mathematical model to understand the dynamics and

development of HIV in vivo during primary phase of infection. Further we develop and improve

this model to include various biological phenomena. We shall also look for models with therapy

and multiple viral strains. With help of tools of differential equation we shall analyse the models

to get meaningful information.


XI

Speaker Profile:

Dr.Brajesh Kumar Kunwar has completed his MBBS from Government College, Thrissur, his

MD from MLN Medical College, Allahabad and his DM in Cardiology from CMC, Vellore. He

has received numerous awards like SN Gupta Award, BN Braun Award, Young Investigator

Award at the 6th International Congress Of Cardiovascular Disease. He has presented 7 papers in

International journals and 1 paper in a national journal. He holds the distinction of being the first

doctor to do EVAR (Endovascular Aortic Repair) and single or dual chamber AICD in coastal

Andhra. In addition he has performed more than 7000 angiograms, greater than 1000 PCI,

greater than 50 renal PTA, greater than 20 ASD device closures, greater than 50 PDA device

closure, greater than 10000 TTE, greater than 350 TEE, greater than 10 AICD.

Dr. Brajesh Kumar Kunwar:

Consultant in Cardiology,

Care hospital, Surat


XII

Advances in cardiac interventional Techniques in

management of structural heart disease

-Dr. Brajesh Kumar Kunwar

Abstract:

The presentation briefly explains about CAD and PAVM. Coronary artery disease (CAD) causes

major death around the world. The symptoms of CAD are chest pain due to narrowing of arteries

in the heart which leads to limitation of blood flow. The corrective measure for the same is

explained with case studies. Pulmonary arteriovenous malformation (PAVM) is the abnormal

communication between pulmonary arties and veins which leads to dyspnea. Closure of the

PAVM with an Amplatzer-type duct occluder was hampered by inability to advance the device

delivery sheath into the PAVM due to vessel tortuosity and inadequate guide wire support. Atrial

septal puncture was performed and a femoral arteriovenous guide wire loop through the right

pulmonary artery, PAVM, and left atrium was created. Traction on both ends of the guide wire

loop allowed advancement of the device delivery sheath into the PAVM and successful

completion of the procedure. Transseptal guide wire stabilization can be a valuable option during

device closure of large PAVMs when advancement, stability, or kinking of the device delivery

sheath is an issue.


XIII

2nd

International Conference on Bio Signals,

Images and Instrumentation

ICBSII - 2015

Research Papers


1

CHARACTERIZATION OF ELECTROHYSTEROGRAMS USING HURST

EXPONENTS Tharini M*, Kamalanand K

Department of Instrumentation Engineering, Anna University, MIT Campus, Chennai, India

ABSTRACT Preterm delivery or birth is described as the delivery of babies who are born alive before 37 weeks of gestation. Term delivery is

the delivery after 37 weeks and before 42 weeks of gestation. Electrohysterograms are the signals representing the uterine muscle activity

that are used to predict the preterm delivery. In this work, (N=80) electrohysterograms consisting of term and preterm signals have been acquired and the Hurst exponent of each signal has been estimated using four different approaches namely Absolute moment method, Aggregate variance method, Higuchi method and R/S method. Results demonstrate that the estimated Hurst exponents of preterm signals are more persistent than term signals. In this paper, objectives of the study, methodology and significant observations are presented.

Keywords: Electrohysterograms, Hurst exponents, Chaos, Term/Preterm delivery

I. INTRODUCTION

Preterm birth, which is also known as premature birth or delivery, is described by the World Health Organisation (WHO) as the delivery of babies who are born, alive, before 37 weeks of gestation. In contrast, term births are the live births after 37 weeks, and before 42 weeks of gestation [1]. Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. The Electrohysterogram (EHG) is the recording of uterine electrical activity from the abdominal surface [2].

Each measurement is composed of three channels, recorded from 4 electrodes. The first electrode (E1) is placed 3.5 cm to the left and 3.5 cm above the navel. The second electrode (E2) is placed 3.5 cm to the right and 3.5 cm above the navel. The third electrode (E3) is placed 3.5 cm to the right and 3.5 cm below the navel. The fourth electrode (E4) is placed 3.5 cm to the left and 3.5 cm below the navel. The differences in the electrical potentials of the electrodes are recorded, producing three channels: S1=E2–E1 (first channel); S2=E2–E3 (second channel); S3=E4–E3 (third channel) [3]. The electrical activity during pre-term labor is significantly different from the activity of term labor [4].

Preterm birth has adverse effects on the new born, such as increased risk of death and health defects. These include impairments to hearing, vision, the lungs, the cardiovascular system and non-communicable diseases. Also up to 40% of survivors of extreme preterm delivery can develop chronic lung disease. In other cases, survivors suffer with neuro-developmental or behavioural defects, including cerebral palsy, motor, learning and cognitive impairments [1].

Nonlinear dynamics, study of systems governed by equations in which a small change in one variable can induce a large systematic change; the discipline is more popularly known as chaos. Unlike a linear system, in which a small change in one variable produces a small and easily quantifiable systematic change, a nonlinear system exhibits a sensitive dependence on initial conditions: small or virtually unmeasurable differences in initial conditions can

lead to wildly differing outcomes. Hurst exponent is used as a measure of the long-term memory of a time series, a statistical methodology for distinguishing random from non-random systems and to identify the persistence of trends [5]. The Hurst Exponent is a dimensionless estimator for the self-similarity of a time series. Initially it was defined by Harold Edwin Hurst to develop a law for regularities of the Nile water level. Now it has applications in medicine and finance. Meaningful values are in the range of 0 to 1 [6].

The objective of this work is to analyze the persistent behaviour of term and preterm electrohysterograms using Hurst exponent.

II. METHODOLOGY

Prerecorded term and preterm EHG signals (N=80) were

obtained from the Physiobank ATM

(http://www.physionet.org/cgi-bin/atm/ATM). Forty two

term and thirty eight preterm EHG signals were used for this study. The first sixty seconds of the adopted signals

was utilized and the Hurst exponents were calculated.

A.Hurst Exponent

The Hurst exponent (H) is used as a measure of the long-term memory of a time series, a statistical methodology for distinguishing random from non-random systems and to identify the persistence of trends. A Hurst exponent value between 0 and 0.5 is indicative of anti-persistent behavior and the closer the value is to 0, the stronger is the tendency for the time series to revert to its long-term means value. A Hurst exponent value between 0.5 and 1.0 indicates persistent behavior; the larger the H value the stronger is the trend. A Hurst exponent close to 0.5 indicates a Brownian time series. In a Brownian time series, (also known as a random walk) there is no correlation between the previous or present observations and a future observation; being higher or lower than the current observation is equally likely [5]. The Hurst Exponent H has the relationship with the fractal dimension D as D=2-H. Initially defined by Harold Edwin Hurst to develop a law for regularities of the Nile water level, it now


2

has applications in medicine and finance. Meaningful values are in the range of 0 to 1 [6].

Definition of the Hurst exponent developed by Harold Hurst is as follows:

R(n) is defined on a time series Xi, i=1, 2, . . . , n as

follows:

R(n) = max (Xi, i = 1, 2, . . . , n) – min (Xi, i = 1, 2, . . . ,

n) . S(n) is the standard deviation and C an arbitrary

constant [6].

B.Adopted methods to estimate Hurst Exponent

In this work, four methods are used to estimate the Hurst exponents which are explained as follows:

i. Aggregated Variance Method:

The original time series is divided into blocks of size m. Then the sample variance within each block is computed. The slope beta=2*H-2 from the least square fit of the logarithm of the sample variances versus the logarithm of the block sizes provides an estimate for the Hurst exponent H [7].

ii. Absolute Value/Moment Method:

The Hurst exponent is estimated from the moments moment=M of absolute values of an aggregated time series process. The first moment M=1 coincides with the absolute value method, and the second moment M=2 with the aggregated variance method. Again, the slope beta=M*(H-1) of the regression line of the logarithm of the statistic versus the logarithm of the block sizes provides an estimate for the Hurst exponent H [7].

iii. Higuchi Method:

This method is very similar to the absolute value method. Instead of blocks a sliding window is used to compute the aggregated series. The function involves the calculation the calculation of the length of a path and, in principle, finding its fractal Dimension D. The slope D=2-H from the least square fit of the logarithm of the expected path lengths versus the logarithm of the block (window) sizes provides an estimate for the Hurst exponent H [7].

iv. Rescaled Range(R/S) Method:

The rescaled range is calculated from dividing the range of the values exhibited in a portion of the time series by the standard deviation of the values over the same portion of the time series. The increase of the rescaled range can be characterized by making a plot of the logarithm of R/S versus the logarithm of n. The slope of this line gives the Hurst exponent, H. If the time series is generated by a random walk (or a Brownian motion process) it has the value of H =1/2 [8].

III. RESULTS AND DISCUSSION

(1)

0 0.5 1 1.5 2 2.5-0.5

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Log o

f A

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om

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0.5

1

1.5

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3

log10(blocks of size m)

log10(R

/S)

R/S Method

slope 1/2

slope 1

(d)

Fig. 1. Typical plots in the estimation of the Hurst exponent of

term EHGs using: (a) Absolute Moment method (b)

Aggregate/Time Variance method, (c) Higuchi method and (d)

R/S method


3

The Hurst exponents of term and preterm electrohysterograms were estimated using four methods in this work.

Fig. 1 and 2 shows typical plots obtained in estimating the Hurst exponents of term and preterm electrohysterograms respectively. Fig. 1(a) and 2(a) shows the typical plot of the variation of Absolute moment as a function of aggregate level, using Absolute Moment method. Fig. 1(b) and 2(b) shows the typical plot showing the variation of variance as a function of aggregate level, using Aggregate Variance method.

Fig. 1(c) and 2(c) shows the typical plot showing the variation of log of curve length as a function of log of aggregate level using Higuchi method. Fig. 1(d) and 2(d) shows the typical plot showing the variation of log of Rescaled range(R/S) as a function of log of block size using R/S method.

Fig. 3 (a), (b), (c) and (d) show the variation of Mean values of Hurst exponents of term and preterm Electrohysterograms obtained using four different methods such as Absolute value method, Aggregate variance method, Higuchi method and R/S method respectively. From Fig. 3, it has been observed that, in all the four methods of determining Hurst exponent, their mean values remained to be close to 0.5 or greater than 0.5 indicating the persistent nature of the Electrohysterograms.

From Fig. 3(a) and Fig. 3(b), it is seen that the Mean values of Hurst exponents of preterm Electrohysterograms are more persistent than that of term Electrohysterograms obtained from all the three channels. From Fig. 3(c) and Fig. 3(d), it is observed that there is no notable variation in the mean values of the Hurst exponents between term and preterm Electrohysterograms indicating equally persistent nature.

Fig. 4. (a), (b) and (c) shows the variation of Hurst exponent as a function of Mean Muscle Force of term and preterm electrohysterograms from channel-1, channel-2 and channel-3 respectively.

From Table 1, it was found that, in all the channels, a positive correlation exists between the Hurst exponent and the mean muscle force for term EHG signals whereas it is negative for preterm EHG signals. However, the correlation between the Hurst exponent and the mean muscle force is found to be very poor (< 0.5).

TABLE. 1. CORRELATION BETWEEN HURST EXPONENT

AND MEAN MUSCLE FORCE

Electrohysterogram

(EHG)

Correlation value

Channel-1 Channel-2 Channel-3

Term EHG 0.2162 0.3226 0.3464

Preterm EHG -0.1031 -0.1977 -0.1749

0 0.5 1 1.5 2 2.50.5

1

1.5

2

2.5

3

Log of Aggregate Level

Log

of A

bsol

ute

Mom

ent

Absolute Moment Method

(a)

(a)

0 0.5 1 1.5 2 2.54

4.5

5

5.5

6

6.5

log10(Aggregate Level)

log1

0(V

aria

nce)

Time Variance Method

(b)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.55

6

7

8

9

10

11

12

13

Log10(Aggregate Level)

Log10(C

urv

e L

ength

)

Higuchi Method

(c)

0 0.5 1 1.5 2 2.5 3 3.5 40

0.5

1

1.5

2

2.5

3

log10(blocks of size m)

log10(R

/S)

R/S Method

slope 1/2

slope 1

(d)

(d) Fig. 2. Typical plots in the estimation of the Hurst exponent of preterm

EHGs using: (a) Absolute Moment method (b) Aggregate/Time Variance

method, (c) Higuchi method and (d) R/S method


4

1 2 30

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)

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PRETERM

(c)

1 2 30

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AN

OF

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RS

T E

XP

ON

EN

T

(R/S

ME

TH

OD

)

TERM

PRETERM

(d)

Fig. 3. Mean values of Hurst exponents obtained by (a) Absolute Value method (b) Aggregate Variance method (c) Higuchi

method (d) R/S method


5

CONCLUSION

Electrohysterograms are the signals representing the uterine muscle activity and are useful in predicting the delivery as Term or Preterm delivery [2]. Preterm delivery is the case where the babies are born alive before 37 weeks of gestation while Term delivery is the delivery after 37 weeks and before 42 weeks of gestation. The Preterm births suffer from health issues such as neuro-developmental or behavioural defects. Therefore, it is necessary to predict the delivery so that, preventive measures in terms of medication and also treatment can be done at an earlier stage for the health issues, in case of predicted Preterm delivery [1].

In this work, the Hurst exponent have been analysed to characterize the persistent nature of term / preterm electrohysterograms. The results demonstrate that the Hurst exponents of the term and preterm electrohysterograms lie in the range of 0.5 to 1. This shows that the electrohysterograms are persistent in nature. Further, it is found that the preterm electrohysterograms are more persistent than the term electrohysterograms. This work is clinically relevant since characterization of the persistent nature of term and preterm electrohysterograms is useful for acquiring diagnostic information.

References

[1]. G. Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, et al., “Prediction of Preterm Deliveries from EHG Signals

Using Machine Learning”, (2013), PLoSONE 8(10): e77154. doi:10.1371/journal.pone.0077154

[2]. Yiyao Ye-Lin, Javier Garcia-Casado, Gema Prats-Boluda, José

Alberola-Rubio, and Alfredo Perales, “Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of

Uterine Contractions”, Computational and Mathematical Methods in Medicine Volume, Article ID 470786, 11 pages,

(2014)

[3]. http://www.physionet.org/pn6/tpehgdb/

[4]. Sindhiya Arora and Girisha Garg, “A Novel Scheme to Classify EHG Signal for Term and Pre-Term Pregnancy Analysis”,

(2012), International Journal of Computer Applications (0975 – 8887) Volume 51– No.18

[5]. Subir Mansukhani, “Predictability of Time Series A statistical measure used to classify time series and infer the level of

difficulty in predicting and choosing an appropriate model for the series at hand”, (2012), The Hurst Exponent: Predictability

of Time Series

[6]. Roman Racine, “Estimating the Hurst Exponent”, April 14, 2011, MOSAIC Group, Prof. Ivo F. Sbalzarini, ETH Zurich

Bachelor Thesis

[7]. http://help.rmetrics.org/fArma/LrdModelling.html

[8]. Hurst, H. E., "Long term storage capacity of reservoirs", (1951), Trans. Am. Soc. Eng., 116: 770–799.

-150 -100 -50 0 50 100 150 200 250 300 3500.1

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1)

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PRETERM

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0.2

0.3

0.4

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0.6

0.7

0.8

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Hu

rst E

xpo

ne

nt (

Ch

an

ne

l-3

)

TERM

PRETERM

(c)

Fig. 4. Hurst exponent as a function of Mean muscle force from

(a)Channel-1, (b)Channel-2 and (c)Channel-3


6

FINGER MOVEMENT RECOGNITION USING

NEURAL NETWORK

Angana Saikia1, Nitin Sahai

1

1 Department of Biomedical Engineering, North East Hill University, Shillong-793022, India

ABSTRACT Biosignals based natural gesture recognition is one of the most active trust in the area of rehabilitation robotics. The work reported

in this paper presents the recognition of index finger movements based on forearm EMG signals. The focus is on derivation of a feature vector for higher recognition rate based on lower number of EMG channels. Feature vector obtained through combination of three time and frequency domain features(modified mean frequency, waveform length and RMS value) resulted into highest recognition rate of

94%.The recognition is through a back propagation neural network trained using L-M and C-G training algorithm. The L-M training algorithm gave the highest recognition rate. The achieved recognition rate is comparable to those reported in literature and better in terms of lower number of channel.

Keywords: EMG, Neural Network, Feature Extraction, Classification, Feature Selection, Feature Combination.

I. INTRODUCTION

The surface electromyogram (EMG) provides a non

invasive method of measuring muscle activity, and has been

extensively investigated as a means of controlling prosthetic

devices. Fig 1 shows the raw EMG signal. Human hand is

an in-dispensable organ of human structure, function, and expression. It is capable of producing complex and

expressive articulations. Physiological variations in the

membranes of muscle fibre cause myoelectric signals. A

technique used for reading and analyzing the myoelectric

signal is called Electromyography (EMG). The EMG

signals are acquired through dual channel Ag/AgCl surface

electrodes. The choice of muscles is based on the type of

movement required. The EMG signals were collected from

ten healthy subjects. EMG pattern recognition is an

advanced, intelligent signal processing technology and has

been proposed as a potential method for reliable user intent

classification [1] [2]. Beyond signal magnitude, a typical pattern recognition algorithm extracts a set of features that

characterize the acquired EMG signals and then classifies

the user's intended movement for external device control.

The benefit of pattern recognition algorithms are that they

can increase the neural information extracted from EMG

signals using a small number of monitored muscles and

allow intuitive control of external devices. Previous studies

have evaluated the ability of various EMG features and

classifiers to recognize user intent [1] [3] [4] [5]. These

studies were mainly done on able-bodied subjects or on

subjects with transradial amputations. The results demonstrated over 90% classification accuracy for either

online or online testing. The comparison of classification

accuracies resulting from utilization of different types of

classifiers and EMG features demonstrated that the type of

classifier used does not significantly affect the classification

performance, while the choice of features has a significant

impact on classification performance [4] [6].

II. MATERIALS AND METHODS

A. EMG signal acquisition and preprocessing

The EMG signals were collected from ten healthy

subjects of age group between 21 to 30. Their right arm is

been cleaned with a cleanser so that the dust particles are

removed. The subject is then asked to sit in a comfortable

state. The EMG signals are acquired through dual channel Ag/AgCl surface electrodes. We have used AD Instruments

PowerLab 4/25T for signal acquisition. Table (1) shows the

specification of the settings of the EMG unit during

acquisition phase. And Table (2) shows the Muscle

selection and electrode placements. In the pre-processing

unit the EMG signal undergo windowing. The raw EMG

signal is first pre-processed using a Rectangular window.

This window is moved along the entire length of the signal,

partitioning the whole signal into smaller signals of equal

length (equal to the length of the window) and individual

partitions are then used for further normalized for

processing. The required figure of the raw EMG signal is in the fig1(a) .

B. Feature Extraction and Classification

To make a system based on EMG first we need to

extract the features of the acquired EMG signal based on

which it can be further classified for various movements.

Here we focus on few of the time domain features and

frequency domain features. Features in time domain are

usually quick and easy to implement as they don’t require

any transformation and hence have lower computational

complexity.

C. Features obtained

Some of the time domain features are: Mean, Root

Mean Square, Variance, Waveform length, Standard

deviation.

* [email protected], [email protected]

mailto:[email protected]


7

And some of the frequency domain features are: Modified

mean frequency, Modified median frequency, Mean

frequency, Median frequency, Power. Classification was done using neural networks. Neural

networks are composed of simple elements operating in

parallel. As in nature, the network function is determined

largely by the connections between elements. Normally neural networks are adjusted, or trained, so that a particular

input leads to a specific target output. There are mainly

three activation functions used in neural network. They are :

Hardlim, Purelin, Logsig. Here in this paper we are using

Back propagation algorithm. The input vectors and

corresponding target vectors are used to train the neural

network until it can approximate a function or associate

input vectors. All the time domain and frequency domain

features were used as the input for the neural network. The

designed network consists of three-layers: input layer, tan-

sigmoid hidden layer and linear output layer. Each layer except input layer has a weight matrix W, a bias vector b

and an output vector a. The weight matrices connected to

inputs called input weights (IW) and weight matrices

coming from hidden layer outputs called layer weights

(LW). Fig 2 shows the Neural Network Architecture.

Levenberg-Marquardt (trainlm) algorithm was utilized for

BP training. It is the fastest method for training of

moderate- sized feed forward neural networks and based on

numerical optimization techniques. This was done by

dividing the training input data: 70% for training, 15% for

validation and 15% for testing. Furthermore, the number of data points in training set was more than sufficient to

estimate the total number of parameters in the network. The

weights and bias of input layer and hidden layer were saved

after each training session. When the simulation results are

not satisfactory, the network trained again with the last

saved weight and bias values. This was done to improve the

network performance and to reduce the number of time for

training. Another type of BP algorithm named scaled

conjugate gradient (trainscg) is also used.

D.Feature Selection and Combination

Feature selection is an optimization problem. Its work is

to search the space of possible feature subsets and pick the

subset that is optimal or near-optimal with respect to a

certain criterion Assuming m features, an exhaustive search

would require, examining all possible subsets of size d and

selecting the subset that performs the best according to the

criterion function. There are two types of evaluation strategies: Filters method, Wrappers methods. Here in this

paper we have used filters method. In filters method

evaluation is independent of the classification algorithm.

The objective function evaluates feature subsets by their

information content, typically interclass distance, statistical

dependence or information theoretic measures.

In order to design an effective object classification system,

feature combination is usually adopted in an attempt to

combine the strengths of multiple complementary features

and produce better performance than any individual feature.

Feature combination is a popular method for improving

object classification performances. There are five types of

feature combination methods [7]: Product, Averaging,

Multiple kernel Learning, LP-B, LP-Beta. Here we have

proposed to use a selection based average combination

algorithm to obtain the best classification performance from

average combination. In average combination we have

taken the average of all the 10 features and have organized

them in descending order. The best three features having

the highest recognition rate has been combined. As we have three features so each features contributes around 33%.


The summary of the classification performance using

back-propagation neural network is shown in tables below

(table 3 and table 4). The network was trained by 10 sets of

data for different movements. Each set consists of input

feature vector obtained from specific type of hand

movement and corresponding output vector. Different

number of hidden neurons selected for both type of BP

training and their classification efficiency are reported.

Feature vector obtained through combination of three time

and frequency domain features (modified mean frequency, waveform length and RMS value) resulted into highest

recognition rate of 94% compared to combination of all 10

features.

IV. CONCLUSION

The objectives of this study is to derive a feature vector

for higher recognition rate based on lower number of EMG

channels. Four index finger movements (flexion, extension,

aduction, abduction) were classified using back propagation

neural network using L-M and C-G training algorithm. Pre-

processed EMG segments of 1sec duration have been used

as test patterns. Ten different features comprising five time domain and five frequency domain features are used in

evaluating the performance of the neural networks. Two

types of training schemes have been employed for training

the neural network. The experimental results show that the

movement detection can be carried out with an accuracy

rate as high as 94% with combination of best three input

features. The levenberg-Marquardt training algorithm

showed the highest recognition rate compared to Scaled-

Conjugate training algorithm

A. Figures and Tables

Fig1:Raw EMG Signal


8

Fig 2: Neural network architecture

TABLE 1. SPECIFICATIONS OF THE SETTINGS OF

THE EMG UNIT DURING ACQUISITION

TABLE 2. MUSCLE SELECTION AND ELECTRODE

PLACEMENT

Electrodes Muscles Function Channel1-lead1 Extensor Digitorum

Muscle Extension of

finger Channel1-lead2 Flexor Digitorum

Superficialis muscle Flexion of

finger Channel2-lead1 Dorsal Interossei Muscle Adduction of

finger Channel2-lead2 Plamar Interossei Muscle Abduction of

finger Ground Styloid Process

TABLE 3. COMPARISON OF RECOGNITION RATES USING

DIFFERENT COMBINATION

Features Flexion Extension Adduction Abduction

Combination

of 10

features

50% 50.5% 54% 54.6%

Combination

of best 3

features

94% 95% 94% 96%

TABLE 4. RECOGNITION RATES OF 10 DIFFERENT FEATURES

Features Flexion Extension Adduction Abduction Modified

Mean

Frequency

75%

80% 85% 90%

Modified

Median

Frequency

50% 45% 70% 65%

Mean

Frequency 40% 25% 10% 25%

Median

Frequency 35% 55% 20% 45%

Power 40% 25% 25% 35% Waveform

Length 75% 70% 70% 85%

RMS 60% 70% 75% 70% Variance 55% 50% 55% 58% Standard

Deviation 20% 50% 40% 40%

Mean 60% 35% 90% 85%

Acknowledgment We acknowledge the department of Electronics and

Communication Engineering, Tezpur University for

providing us proper laboratory facilities to accomplish this

project.

References [1] Choi et al. " Development and quantitative performance

evaluation of a non-invasive EMG computer interface",

IEEE Trans Biomed Eng, 2009, 56:188-191.

[2] Englehart et al."A robust, real-time control scheme for multifunction myoelectric control", IEEE Trans Biomed

Eng, 2003, 50:848-854.

[3] Englehart et al. " Classification of the myoelectric signal

using time-frequency based representations", Med Eng

Phys, 1999, 21:431-438.

[4] Finley et al. "Myocoder studies of multiple myocoder

response", Archives of Physical Medicine and

Rehabilitation, 1967, 48(supple3): 598.

[5] Chan et al. "Fuzzy EMG classification for prosthesis

control", IEEE Transactions on Rehabilitation

Engineering,2000, 8(supple3):305-311.

[6] Farry et al. "Myoelectric teleoperation of a complex robotic hand", IEEE Transactions on Robotics and

Automation, 1996,12(supple5):775-788.

[7] Gehler et al. "On Feature Combination for Multiclass

Object Classification", Max Planck Institute for Biological

Cybernetics Spemannstr. Tubingen, Germany, 38, 72076.

Parameters Values

High cut off frequency 400 Hz

Low cut off frequency 40 Hz

Notch cut off frequency 50 Hz

Amplification rate 5

Common mode rejection ratio 110db


9

DEVELOPMENT OF A SYSTEM FOR ANALYSIS OF REMOTE

AUSCULTATION SIGNAL FOR MANAGEMENT OF CHRONIC

ASTHMA CASES

Bony George1, Ajith K.G

2., George Varkey

3, Rajesh M

4

1,2,3 NIELIT, Calicut, Kerala

4 Mobilexion Technologies

ABSTRACT

Asthma is a chronic pulmonary disease affecting millions around the globe. World Health Organization estimates that roughly the equivalent of the population of the Russian Federation suffers from asthma and that this number is rising. World-

wide, deaths from this condition have reached over 180,000 annually. Asthmatic condition reduces quality of life. Pulmonary auscultation using a stethoscope is the common method of diagnosis. However, there are two major limitations. (a) The transient nature of the attack makes it difficult to observe the actual condition by the doctor, unless the patient is admitted (b) The process is subjective (depends on the individual's own hearing, experience and ability to differentiate between different sound patterns). We have developed a low cost embedded system for acquisition, storage, analysis and real-time transmission of auscultation signals that can help in solving both these problems. A custom made stethoscope is used to acquire and store the auscultation signal into the receiving segment of this system. The stored packets are routed to a smart phone that performs local analysis at patient end and real-time transmission to a cloud server and a receiving smart phone at the doctor’s end. The setup allows the

doctor at a remote location to listen to the pulmonary auscultation in real-time so as to decide on the optimum care regime. The analysis and visualizations provided by the software brings in more objectivity for this decision making, by its provisions for comparing current signal and its parameters with previously stored ones.

Keywords: Asthma, Auscultation, Stethoscope, Acoustic Analysis, Spectrogram

I. INTRODUCTION

Asthma is a disease affecting the airways

that carry air to and from the lungs. People who

suffer from this chronic condition (long-lasting or recurrent) are said to be asthmatic. The inside walls

of an asthmatic's airways are swollen or inflamed.

This swelling or inflammation makes the airways

extremely sensitive to irritations and increases

susceptibility to an allergic reaction. The

inflammation causes the airways to become narrower.

Symptoms of the narrowing include wheezing (a

hissing sound while breathing), chest tightness,

breathing problems, and coughing. Asthmatics

usually experience these symptoms most frequently

during the night and the early morning.

Part A of Fig 1 shows the location of the

lungs and airways in the body. Part B shows a cross-

section of a normal airway. Part C shows a cross-

section of an airway during asthma symptoms.

Fig.1:

Physiology of Cause of Asthma

When the airways react, the muscles around

them tighten. This narrows the airways, causing less

air to flow into the lungs. The swelling also can

worsen, making the airways even narrower. Cells in

the airways might make more mucus than usual that

further narrows the airways.

Sometimes asthma symptoms are mild and

go away on their own or after minimal treatment.

Other times, symptoms continue to get worse.

Asthma attacks also are called flare-ups or

exacerbations. Severe asthma attacks can be fatal and

may require emergency care. *[email protected],[email protected]

,[email protected],[email protected]


10

According to WHO estimates, 255,000

people died of Asthma in 2005. Over 80% of this is

from low and lower-middle income countries [3].

Asthma creates a substantial burden on individuals

and families as it is more often under-diagnosed and

under-treated. In India, an estimated 57,000 deaths were attributed to Asthma in 2004 and it was seen as

one of the leading cause of morbidity and mortality in

rural India.

Some of the facts about asthma brought out

by WHO illustrate the severity of the problem [2].

Around 8% of the Swiss population suffers from

asthma as against only 2% some 25-30 years ago. In

Western Europe as a whole, asthma has doubled in ten years. In the United States, the number of

asthmatics has leapt by over 60% since the early

1980s and deaths have doubled to 5,000 a year. There

are about 3 million asthmatics in Japan of whom 7%

have severe asthma. In Australia, one child in six

under the age of 16 is affected. India has an estimated

15-20 million asthmatics. Prevalence is between 10%

and 15% in 5-11 year old children.

Being a chronic condition, continuous

medical care is required for asthma. The disorder can

be adequately controlled with drugs. Under diagnosis

and inappropriate therapy remain the major cause of

Asthma morbidity and mortality.

Asthma diagnosis is done by listening to the

internal sounds of the lungs using a stethoscope, which is known in medical terms as auscultation. The

clinician listens to the different sounds in the lungs

corresponding to the inhalation and exhalation and

based upon that decides the intensity of asthma.

One problem with the above is that most of

the time patients do not have obvious asthma

symptoms when they arrive at the doctor’s office. For

instance, you may have coughed and wheezed for a week, and by the time you see your doctor, you have

no symptoms at all. Then suddenly, when you least

expect it, you might have asthma attack

symptoms such as shortness of breath, coughing, and

wheezing. Sometimes allergies to seasonal pollen or

weather changes can trigger asthma attack symptoms.

Other times, a viral infection such as cold or flu can

trigger it. Even exercise or sudden stress or allergies

to aspirin or other medications can cause asthma

attack symptoms.

The above transient nature of the attack is a

major problem for the management of asthma cases.

The attack may occur in the middle of the night far

away from a hospital. The patient is unable to

communicate coherently and the by-stander is unable

to determine the severity of the problem. The only

solution is to depend on the experience of the

clinician at the other end of the telephone line and

many times there are serious gaps in the

communication leading to non-optimum decisions.

Development of appropriate low cost

clinical instruments to be used in the home

environment will be a great help in the above

scenario [8][10].We are in the process of developing

a low cost device for acquisition, storage, analysis

and real-time transmission of auscultation signals for

usage in the home. A custom made chest-piece is

used to acquire and store the auscultation signal as small packets of digital data. These are routed to a

smart phone that performs local analysis at patient

end [11][12]. In case GPRS connectivity is available

at home, the packets are transferred in real time to a

cloud server. A smart phone at the doctor’s end can

pick this up to allow him to listen to the pulmonary

auscultation in real-time so as to decide on the

optimum care regime.

This paper presents the system developed

along with analysis schemes possible with it. It is

organized as follows. Section II describes the

characteristics of the signal and the type of

information that we wish to collect from it. Section

III gives an overview of the system hardware. Section

IV gives an overview of analysis software. Section V

gives a few typical visualization results. The system

is currently under development and the analysis

segment is continuously under improvements as per

the requirements of the consulting clinicians.

II. IMPORTANCE OF LOW FREQUENCY

BIO ACOUSTIC SIGNAL IN CHARACTERIZATION OF

ASTHMA CASES

The diagnosis of chest diseases is facilitated by pulmonary auscultation using a stethoscope. This

device, invented in 1821 by the French Physician,

Laennec, is still the commonest diagnostic tool used

by doctors. Major lung sounds include:

A. Crackle sound

These are discontinuous lung sounds. It is

explosive and transient in nature. Its frequency range

is from 10 to 2000Hz and duration is typically 20 ms.

The number of occurrences and the number of the

crackles in each are indicators of the type of

disease.Crackles can be found in many diseases like

Heart congestion failure, Pneumonia, Bronchiectasis,

Pulmonary fibrosis etc. They are an early sign for


11

respiratory diseases, since fine crackles are originated

in small air paths. [2][3]

B. Wheeze

Wheezing results from a narrowing or

partial blockage (obstruction) somewhere in the

airways. The narrowing may be widespread (as in

case of asthma, chronic obstructive pulmonary

disease [COPD], and some severe allergic reactions)

or only in one area (as may result from a tumor or a

foreign object lodged in an airway). The high-pitched whistling sound while you exhale - or wheezing - is a

key sign of both an obstructed airway and asthma.

Wheeze can be heard at the chest and the

trachea. It is a high-pitched sound with dominant

frequency at 300 Hz or more with a duration > 250

ms[1].

It is clear from the above that the low

frequency segment of the auscultation signal is of

primary importance in the characterization of asthma

cases. A clinician uses his previous experience in

identifying these segments for characterizing the

patient condition. This method, even though time

tested and well accepted, has many limitations. It is a

subjective process that depends on the individual's

own hearing, experience and ability to differentiate between different sound patterns. It is not easy to

produce quantitative measurements or make a

permanent record of an examination in documentary

form. Long-term monitoring or correlation of

respiratory sound with other physiological signals is

also difficult.

Over the last 30 years, computerized

methods for the recording and analysis of respiratory sounds have overcome many limitations of simple

auscultation. Respiratory acoustic analysis can now

quantify changes in lung sound, make permanent

records of the measurements made and produce

graphical representations that help with diagnosis and

management of patients suffering from chest diseases

[3]. However, these require costly instrumentation

normally found only in specialist hospitals.

The advent of low cost electronic devices

and advances in mobile and cloud computing now

makes it possible to provide such methods even in the

home settings. The EpionexTM Tele-stethoscope

(ETS) is a system being developed by us in the above

lines. The next two sections provide the hardware

and software details of ETS.

III. ETS - SYSTEM HARDWARE

Fig. 2 gives the block diagram of ETS

system hardware. It begins with the stethoscope chest

piece which is used as the sensor.The chest-wall

movements are so weak that a free-field recording is

not possible. It is essential to couple the diaphragm

acoustically with the chest wall through a closed air

cavity. Vibrations of the chest wall are converted into

pressure variations of the air in the stethoscope. An

electret microphone connected to the end of the chest

piece. Changing the distance between the two plates of a charged capacitor inside the microphone induces

a voltage fluctuation. This low amplitude signal is

amplified with a low noise audio amplifier and

filtered using a bandpass filter so that signal of

interest is amplified and unwanted noise is removed.

Fig.2 Overall system architecture

The filtered and amplified signal is sampled

using an analog to digital coverter (ADC) in the

microcontroller with 16 bit resolution. The resulting

digital signal stream is bufferred, packetized and

transferred to the other end of the set-up in real time

over 3G/GPRS communication channels. This is

achieved by first uploading it to a cloud server from

where it is downloaded to the doctor’s end. Here the

packets are decoded, reassembled and streamed to a DAC connected to the headphone of the doctor, so

that he can listen to the chestpiece. Additionally, he

can comminicate with the patient side using VoIP

communication.

Figure 3 gives the preamplifier and filtering

circuit used in the system.Op-amp OPA2336 based

active filter and amplifier circuit is designed with a

passband frequency of 3 to 2000 Hz so that all the relevent frequency components are included[4].The

amplifier provides a gain of 56 dB. Op-amp OPA336

from Texas Instruments is used. It’s features like low


12

power, low noise and single supply makes it ideal for

our purpose.

Fig.3 Filter amplifier

IV. ETS – SOFTWARE

STRUCTURE

Fig 5 gives the segments of the ETS software. It

consists of home segment, cloud segment and

clinician segment. The home segment system consists

of indipendent applications for signal acquisition,

transportation, analysis and coordination. The signal

acquisition application acquires the signal and stores

it in local database. It additionally sends it to the transport application which sends it to the cloud

server and to the smart phone of the remote clinician,

both over the GPRS link. The analysis application

computes the signal parameters and provides reports

and charts for usage by the patient. The cordination

application manages the communication with the

remote clinician. It uses VoIP for voice

communication and data packets for commands.

Using the latter, the doctor is able to take control over

the software so that he can visually indicate the

location of placing the chest-piece on the body of the patient. It also provides chat mode to augment voice

communication.

The cloud segment manages multiple requests from

many clients. It decodes the request packets to

determine the type of request and in case of a request

for DBMS entry, decodes the same and send to the

requesting entity. In case of a signal packet, it

additionally computes standard parameters and store in the DBMS.

Fig.5(a) Software System - Home Segment

Fig.5(b) Software System - Cloud Segment

The clinician segment provides tele-medicine

services to the remote patient. It decodes the packets

and in case of a voice packet depending on the

current state directs it to the head-phone. The state

could be either VoIP or Auscultation. This

management ensures that these voice streams are not

mixed up. It also has an analysis segment to view the

results of signal analysis to compare with previous values.


13

Fig.5(c) Software System - Clinician Segment

A number of analysis techniques are

provided to display the time domain signal, convert it

to spectral domain, and display the important

parameters. A number of researchers have come out

with various algorithms for determination and characterization of the signal [6][7][8][9]. Majority of

them uses Short Time Fourier Transform (STFT) as

the primary step in the analysis[4]. It computes the

signal energy distribution in the joint timefrequency

domain.The time-dependent Fourier transform is the

discrete-time Fourier transform for a sequence,

computed using a sliding window. The sliding

window divides the signal into several blocks of

data.Then an N point fast Fourier transform is applied

to obtain the frequency contents of each block ofdata,

where N is frequency. It aligns the centre of the first

sliding window with the first sample of the signal X and extends the beginning of the signal by adding

zeros. The sliding window moves time steps samples

to the next block of data. If the window moves out of

X, the signal is padded with zeros.

We thus have the time domain as well as the

frequency domain representations of the input signal.

This is now used to compute various parameters like dominant frequency within a given band, signal

energy, etc. We can also use these for detection of

weezing instances, cracles within a segment, etc.

These too are presented to the clinician for his

decision making.

V. USE CASE SCENARIOS

Fig 6 gives two use case scenarios using the

spectrogram provided by ETS. The pseudo colouring

used is arbitrary and may be modified based on

recommendation from clinical community.

Fig 6(a): Spectrogram of Volunteer 1

Fig 6(b): Spectrogram of Volunteer 2

In order to remove the influence of heart

sounds and other unwanted lung noises, the

frequency below 100 Hz are masked from the

spectrogram. A distinguishable amount of wheezes

are detected from the second volunteer’s

Spectrogram. Detailed trial with multiple patients is

needed to validate the utility of these for actual

diagnosis.

VI. CONCLUSION

Developing the system for the analysis of

low frequency bio acoustic signals is very useful for

the computer enabled diagnosis, characterization and

management of chronic asthma cases. We have

developed a system for real-time remote acquisition,

storage, analysis and transport of this signal for tele-

medicine application. The work on the development

of analysis methods are continuing. We hope that the

system will help in popularizing these analysis

methods so that remote management of asthma

patients will become more optimal in near future.


14

Acknowledgement

This paper is the outcome of a collaborative effort

from two institutions -National Institute of

Electronics and Information technology (NIELIT)

Calicut, and Mobilexion Technologies Pvt. Ltd

(Mobilexion) Trivandrum. We are deeply indebted to

the management of these institutions for having

provided a congenial atmosphere for undertaking this

work.

References

[1]. Abhishek Jain, Jithendra Vepa “Lung sound analysis for wheeze

episode detection” 30th Annual International IEEE EMBS

Conference Vancouver, British Columbia, Canada, August 20-24,

2008

[2]. Bronchial asthma, World health organization FactSheetN206

http://www.who.int/mediacentre/factsheets/fs206/en/

[3]. Sovijarvi, A. R. A., Malmberg, L. P., Charbonneau,Vandershoot,

J., Dalmasso, F., Sacco, C., Rossi, M., Earis, J.E.“Characteristics

of breath sounds and adventitious respiratory sounds”, EurRespir

Rev 2000; 10: 77, 591-596

[4]. Moussavi, Zahra “Fundamentals of Respiratory Sounds and

Analysis” Morgan & Claypool, ISBN 1598290975, 2006

[5]. Jayant V. Mankar, Praveen Kumar Malviya “Analysis of Lung

Diseases and Detecting Deformities in Human Lung by Classifying

Lung Sounds”. International Conference on Communication and

Signal Processing, April 3-5, 2014, India

[6]. Victor Grinchenko,Alexander Artemiev,Oleg Gurenko,Rustam

Nabiev,Anna Glazova “Mobile End user solution for system of

monitoring of Respiratory and cardiac sounds” IEEE XXXIV

international scientific conference electronics and nanotechnology

2014

[7]. R.Jane, S.Cortes, J.A. Fiz, J. Morera “Analysis of Wheezes in

Asthmatic Patients During Spontaneous Respiration.” Proceedings

of the 26th Annual International Conference of the IEEE EMBS

San Francisco, CA, USA • September 1-5, 2004

[8]. Sibghatuallah I. Khan, Naresh P. Jawarkar “Cell phone based

Remote Early Detection of Respiratory Disorders for Rural

Children using Modified Stethoscope” International Conference on

Communication Systems and Network Technologies 2012.

[9]. Saba Emrani, and Hamid Krim. “Wheeze detection and location

using spectro-temporal analysis of lung sounds.”29th Southern

Biomedical Engineering Conference-2013.

[10]. Md. Sarwar Jahan and A. B. M. Aowlad Hossain.“A Low Cost

Stethoscopic System for Real Time Auscultation of Heart

Sound”3rd International Conference on Informatics, Electronics &

Vision, 2014.

[11]. Gary Comtois, John I. Salisbury, and Ying Sun. “A Smartphone-

Based Platform for Analyzing Physiological Audio Signals.” 38th

Annual Northeast Bioengineering Conference (NEBEC), 2012

[12]. Ji Yun Shin, SehiL'Yi, Dong Hyun Jo, Jin Ho Bae, and Tae Sao

Lee. “Development of Smartphone-based Stethoscope

System”.13th International Conference on Control, Automation

and Systems-2013


15

* [email protected]

FPGA IMPLEMENTATION OF DISTRIBUTED CANNY EDGE DETECTOR

ALGORITHM

DHINAKARAN M1, C. M. SUJATHA

2

1,2College of Engineering, Anna University, Chennai

ABSTRACT In this work implementation of distributed Canny edge detector algorithm onto FPGA platform is attempted. This algorithm computes

thresholds at the block-level based on non-uniform gradient magnitude histogram. Latency is now a function of block size instead of frame size, and is significantly reduced in this algorithm. The proposed algorithm takes 0.729ms with 2538 slices to detect edges when clocked at

50KHz. Fast edge detection of images and videos with high resolutions including full high definition is supported by this algorithm. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of proposed algorithm is better than the conventional frame-based algorithm especially when noise is present in images.

Keywords: Canny Edge Detector, FPGA, Histogram

I. INTRODUCTION

Edge detection is a very important first step in image

processing applications. Many types of edge detection

algorithms, such as Prewitt, Robert, Sobel, Gauss-Laplacian, Kirsch and Canny edge detectors [6] are

designed to detect the step response due to the edge

location in the discontinuous part of the image. However,

these operators are sensitive to noise and the edge detected

is discontinuous. Among these algorithms, Canny

algorithm has been widely used in many real-world

applications due to its ability to extract significant edges

with good detection and localization performance. This

algorithm has the advantages of good signal to noise ratio,

and fine edge thinning.

Canny algorithm detects edge using gradients and hysteresis estimation. The image gradients are estimated

using different gradient operators [11]. The first-order

derivative does not guarantee the detection the edges that

map with continuous edge contours and unwanted

branches. The second-order derivative involving zero

crossing suffer from generating erroneous edges due to its

high sensitivity to noise. Gradient magnitudes of four

nearest neighbors along the direction are necessary to

compute two intermediate gradients. The hysteresis

thresholding is obtained by computing the higher and

lower thresholds for edge detection based on the entire

image statistics [8].

Recently, Field Programmable Gate Array (FPGA)

technology has become a viable target for the

implementation of image processing algorithms. Image

processing is difficult to achieve on a serial processor. This

is due to the large data set required to represent the

image and the complex operations that need to be

performed on the image [7]. Many hardware

implementations of Canny edge detection algorithm have

been proposed. The correct setting of the threshold affects

the performance of this algorithm greatly and the

computational cost is very high for real time

implementation [17].

The implementation of Canny edge detection algorithm

onto FPGA-based platforms involves translation of software design directly into VHDL or Verilog using

system-level hardware design tools, which results in a

decreased timing performance. The parallel implementation

proposed by Qian Xu, operates on only 4 pixels in parallel,

resulting in an increase in the number of memory accesses

and in the processing time when compared to the

conventional algorithm. Furthermore, all of these

implementations compute the high and low thresholds off-

line and use the same fixed threshold values for all the

images in order to reduce the computational complexity.

However, this results in a decreased edge detection performance.

When compared to other edge detection algorithms,

Canny edge detection algorithm also involves many pre-

processing and post-processing steps. In order to reduce

memory requirements, latency and increase throughput, a

distributed canny edge detection algorithm is proposed

[10]. In this algorithm, the threshold selection algorithm is

based on the distribution of pixel gradients in a block of

pixels. The threshold is calculated using the data of the

histogram of gradient magnitude instead of being set

manually in a failure or try fashion, and can give quite good

edge detection results without the intervening of an operator.

As the hysteresis thresholds calculation is based on a

very finely and non-uniformly quantized 64-bit gradient

magnitude histogram, this calculation is computationally


16

expensive and thereby, hinders the real-time

implementation. To overcome this, an adaptive iterative

threshold selection algorithm proposed in [18] has been

adopted. This algorithm computes the high and low

threshold for each block based on the type of block and the

local distribution of pixel gradients in the block. This algorithm can be mapped onto reconfigurable hardware

architecture efficiently.

Each block can be processed simultaneously, thus

reducing the latency significantly. Furthermore, this allows

the block-based canny edge detector to be pipelined very

easily with existing block-based codec, thereby improving

the timing performance of image/video processing systems

[19].

II. METHODOLOGY

A. Distributed Canny Edge Detector Algorithm

Canny developed an approach to derive an optimal

edge detector based on the criteria related to the detection

performance.

Fig 1: Block Diagram of Proposed Canny Edge Detector Algorithm

The workflow consists of smoothing, finding gradients,

direction calculations, non-maximal suppression, double

thresholding and edge tracing by hysteresis.

B. Pixel Classification

The computing engine local memory 1 consists of m x

m overlapping block, which determines the block type.

There are two stages in block classification units

architecture 1) Pixel classification, 2) block classification.

The local variance of each pixel is utilized. It takes one

adder, two accumulators, two multipliers and one squarer to

perform this computation. Two counters are involved in

counting total number of pixels under each pixel type. The

counter 1 generates the number of uniform pixels C1 and

the counter 2 generates the number of edge pixels C2. Once C1 and C2 are obtained the second classification stage is

initialized. The outputs obtained are given as control signals

to mux1 and mux2 to identify the value of P1. Finally, in

order to produce the enable signal EN, signal P1 is

compared with 0. EN is activated if the P1 value is larger

than 0.

Fig2: Block Diagram of Block Classification Unit (Qian X et.al 2014)

Once EN is activated, the gradient calculation,

magnitude calculation, direction, non-maximal suppression

and low threshold calculation and thresholding with hysteresis units are enabled. If P1 value is less than 0, the

above units are disabled.

C. Gradient Calculation-Vertical and Horizontal

Gradient

After smoothing the image and eliminating the

noise, the next step is to find the edge strength by taking the gradient of the image. The Sobel operator

performs a 2-D spatial gradient measurement on an image.

The gradient magnitude is given by:

|G| = |Gx| + |Gy| (1)

The Sobel operator uses a pair of 3x3 convolution

masks, one estimating the gradient in the x-direction

(columns) and the other estimating the gradient in the

y-direction (rows).

This block calculates the vertical and horizontal

gradients using 3 x 3 convolution kernels. An 8-bit pixel in

row order of the image produced during every clock cycle

in the image smoothing stage is used as the input in this stage. Since 3 x 3 convolution masks are used to calculate

the gradients, neighboring eight pixels are required to

calculate the gradient of the center pixel.

The output pixel produced in previous stage is a pixel

in row order. In order to access 8 neighboring pixels in a

single clock cycle, two First In First out (FIFO) buffers are

employed to store the output pixels of the previous stage.


17

The gradient calculation introduces negative numbers.

Negative numbers can be handled easily by using signed

data types. Signed data means that a negative number is

interpreted as the 2’s complement of number. In the design,

an extra bit is used for signed numbers as compared to

unsigned 8 bit numbers i.e. 9 bits are used to represent a gradient output instead of 8. Two gradient values are

calculated for each pixel, one for vertical and other for

horizontal. The 9 bits of vertical gradient and the 9 bits of

the horizontal gradient are concatenated to produce 18 bits.

Since the whole design is pipelined, an 18-bit number is

generated during every clock cycle, which turns out to be

the input of the next stage.

D. Calculation of Gradient Angle

Finding the edge direction is trivial once the

gradient in the x and y directions are known, then

computation of gradient angle is calculated. The edge

direction will equal to 90 or 0 depends on Gx and Gy

value. 0 depend on Gy value and 90 depend on Gx value.

The formula for finding the edge direction is given below:

(2)

E. Tracing Edge Angle

Once the edge direction is known, the next step is

to relate the edge direction to a direction that can be

traced in an image. Any edge direction falling between

to & to is set to . Any edge direction

falling between to is set to . Any edge

direction falling between to is set to .

And finally, any edge direction falling between to

is set to .

Fig3: Block Diagram of Magnitude Angle Calculation (Qian X et.al 2014)

The bigger the matrix, the more number of angles

one could get, which implies the edge angle will be

more precise, i.e. it will follow the edge better. On the

down side, it will be a bigger computation task as now the

kernel/mask size is bigger.

F. Non-Maximal Suppression

After the edge directions are known, non-maximal

suppression is applied. Non-maximal suppression is used

to trace along the gradient in the edge direction and

compare the value perpendicular to the gradient. Two

perpendicular pixel values are compared with the value in

the edge direction. If their value is lower than the pixel on the edge then they are suppressed i.e. their pixel value is

changed to 0, else the higher pixel value is set as the edge

and the other two suppressed with a pixel value of 0. Then

the points along the curve where the magnitude is

biggest are marked, by looking for a maximum along a

slice normal to the curve (non-maximum suppression).

These points should form a curve.

Fig4: Concept of Non-Maximal suppression operation

The non-maxima elimination filter is used in order to

eliminate pixels that are not part of a continuous line. In

other the pixels which are having high gradient magnitude

but they are not a part of a continuous line can be easily

eliminated using this filter. If the current pixel and the

values of the derivatives at that pixel are Gx and Gy, the

direction of the gradient can be approximated to any one of

the sectors.

Once the direction of the gradient is known, the values of the pixels found in the neighborhood of the pixel under

analysis are interpolated. The pixel which has no local

maximum gradient magnitude is eliminated. The

comparison is made between the actual pixel and its

neighbors along the direction of the gradient.

Since the gradient calculation depends on the direct

neighborhood pixels, a 3 x 3 window operator is used. The

output produced along the vertical and horizontal stage is

an 18-bit number, first nine bits are horizontal gradient and

other nine bits are vertical gradient.


18

Fig 5: Block Diagram of Non-Maximal Suppression

In order to access all the pixels in the 3 x 3 window at

the same time, two 18-bit FIFO buffers each having width

of image minus three array size are employed. To calculate

the phase and magnitude at each and every pixel, the

horizontal and vertical gradient values derived from the

eighteen bit number are used. The output produced in this

stage is subjected to a threshold and stored in the memory.

Instead of implementing each stage separately, the

pipelined design utilizes the resources efficiently on the FPGA and also increases the execution speed of the

algorithms, because the output is produced each and every

clock cycle with an initial latency till the pipe is full.

G. Hysteresis threshold

Hysteresis is used as a means of eliminating streaking.

Streaking is the breaking up of an edge contour caused by the operator output fluctuating above and below the

threshold. If a single high threshold is applied to an

image and an edge has an average strength equal to

high threshold, then due to noise, there will be

instances where the edge dips below the threshold.

Fig6: Block diagram of Threshold Calculation

To avoid this, hysteresis uses 2 thresholds, a high and a

low. Any pixel in the image that has a value greater

than high threshold is presumed to be an edge pixel, and

is marked as such immediately. Then, any pixels that are

connected to this edge pixel and that have a value

greater than low threshold are also selected as edge

pixels. The Altera DE2 kit includes a VGA display port via

a DB15 connector.

III. RESULTS

The distributed Canny edge detection algorithm is

simulated on MATLAB and modules were written and

tested in Verilog HDL.

A. Simulation Results

The edge detection scheme developed in this work

is first implemented using MATLAB software tool and tested for many images.

(A) (B)

(C) (D)

(E) (F)

Fig9 (A): Input Lena image, Fig9 (B): Lena image with 90% noise, Fig9 (C): Conventional Output Image of Canny Edge

Detection without noise, Fig9 (D): Conventional Output Image of Edge Detection with noise, Fig9 (E): Conventional Output Image

of Canny Edge Detection without noise, Fig9 (F): Proposed Output Image of Edge Detection with noise


19

Canny edge detection algorithm is performed with help

of 480 × 640 Lena image is shown in Fig (9A) obtained

from USC SIPI database along with the image corrupted

with 90% noise as in fig (9B). The horizontal gradient Gx

and vertical gradient Gy of these images are calculated at

each pixel location by convolving the image with gradient masks. Gradient calculation, non-maximal suppression is

performed.

Then output images of Lena obtained from

conventional Canny edge detector algorithm using 480 × 640 are shown in fig (9C and 9D) without and with noise

respectively. Fig (9E and 9F) represents output images

obtained using distributed Canny edge detection algorithm

without and with noise respectively which results in a

significant speedup without sacrificing the edge detection

performance. The input image segmented into 64 × 64

blocks then Canny edge detection is performed. The result

shown this proposed algorithm could detect edges properly

even for noise corrupted images.

B. Synthesis Results

The project has been implemented on an Altera DE2

board. The inherent features of this board make it suitable

for our application. The DE2 board has the Cyclone II

processor. DE2 board provides users many features to

enable various multimedia project developments.

TABLE I

Device Utilization Summary

Logic

Element

Combination

Function

Detected

Logic Reg

Total

Memory (bits)

Number

of Slices

4580 3198 4314 336725 2538

TABLE I presents resource usage and performance for

distributed canny edge detection algorithm.

Fig10: Test Bench Waveform of Proposed Algorithm

Simulations in Xilinx using fixed point operations

generated results for the 480 × 640 image using the proposed distributed Canny edge detector with block size of

64×64 and a 3×3 gradient mask.

(A) (B)

Fig11 (A): Input Lena image via VGA port Fig11 (B): Output Image of Distributed Canny Edge Detection via

VGA port

Result is obtained by loading the text file containing

the output image pixels onto MATLAB, and displaying the

output image. . Horizontal and vertical gradient magnitudes

are fetched from local memory and used as input to non-

maximal suppression unit. It computes gradient direction at each pixel and suppress all the non-maximal values.

TABLE II

PSNR Table

Sample

Image

Time

elapsed

MSE PSNR

Lena 0.7295 3.7189 38.4267

Fruits 0.7389 3.6982 37.4509

Barbara 0.7539 3.7245 37.4201

Peppers 1.1278 5.7189 36.5577

Baboon 0.7574 3.3603 37.8670

IV. CONCLUSION

The implementation of distributed Canny edge detector

algorithm on FPGA is carried out using Altera DE2.

The steps that were implemented in hardware were

approximated and simplified appropriately for a cost and

time efficient implementation. The architecture can be used

for any size images by adjusting the width of generic

memory modules. This architecture also capable to operate

at higher frequencies. Based on the timing results, computation of magnitude and direction of gradient steps of

the distributed canny edge detector algorithm are faster in

hardware. The resulting optimized enhanced image fits on a

FPGA such as Altera DE2, with sufficient resources

available for to make use of the desired edge information.

The simulation and synthesis results were obtained and

designs are successfully validated using hardware

simulation feature of using Cyclone II family chip type on

development kit type DE2. In order to reduce the large

latency and meet real time requirements, the distributed

Canny edge detector algorithm presented, to compute edges


20

of multiple blocks at the same time. Most importantly,

conducted conformance evaluations and subjective tests

show that, compared with the frame based canny edge

detector, the proposed algorithm yields better edge

detection results for both clean and noisy images.

Distributed Canny yields advantages such as low latency, better performance, pipelining. The algorithm is mapped

into the Quartus Cyclone 2 FPGA platform and tested using

ModelSim. The synthesized results shows 4314 slice

utilization and 336725(bits) memory utilization. The

proposed architecture takes only 0.729ms to detect edges of

480 × 640 images in USC SIPI data base when clocked at

50MHz.

REFERENCES

[1] Alzahrani F.M, and Chen T, “A real-time edge

detector algorithm and VLSI architecture,” Real-

Time Imaging, vol. 3, no. 5, pp. 363 – 78, 1997.

[2] Benedetti, A., Perona, P.: “Real-time 2 -D Feature

Detection on a Reconfigurable Computer,”

Proceedings of the 1998 IEEE Conference on

Computer Vision and Pattern Recognition, 1998.

[3] Chou, C., Mohanakrishnan, S., Evans, J.: “FPGA

Implementation of Digital Filters,” Proc. ICSPAT,

1993.

[4] Gentsos, C., Calliope, L.S., Spiridon, N., and

Vassiliadis, N., “Real Time Canny Edge

Detection Parallel Implementation for FPGAs”.

17th International Conference on Electronics,

Circuits, and Systems (ICECS), Athens 2010 IEEE.

[5] Lorca, F.G., Kessal, L., and Demigny, D., “Efficient

ASIC and FPGA implementation of IIR filters for

real time edge detection,” in Proc. IEEE ICIP, vol. 2.

Oct. 1997, pp. 406–409.

[6] Lourenco, L.H.A., “Efficient implementation of

canny edge detection filter for ITK using CUDA,”

inProc. 13th Symp. Comput. Syst., 2012, pp. 33–40.

[7] Luo, Y., and Duraiswami, R., “Canny edge detection

on NVIDIA CUDA,” in Proc. IEEE CVPRW, Jun.

2008, pp. 1–8.

[8] Narvekar, N.D., and Karam, L.J., “A no-reference

image blur metric based on the cumulative

probability of blur detection (CPBD),”IEEE Trans.

Image Process. vol. 20, no. 9, pp. 2678–2683, Sep.

2011.

[9] Neoh H, and Hazanchuck A, “Adaptive edge

detection for real-time video processing using

FPGAs,” Altera Corp., San Jose, CA, USA,

Application Note, 2005.

[10] Park, I. K., Singhal, N., Lee, M.H., Cho, S., and

Kim, C.W., “Design and performance evaluation of

image processing algorithms on GPUs,” IEEE Trans.

Parallel Distrib. Syst., vol. 22, no. 1, pp. 91–104,

Jan. 2011.

[11] Peter Mc Curry, Fearghal Morgan, Kilmartin, L.,

“Xilinx FPGA implementation of a pixel processor

for object detection applications”. In the Proc. Irish

Signals and Systems Conference, Volume 3,

Page(s):346 – 349, Oct. 2001.

[12] Qian Xu., Srenivas, V., Chaitali, C., Fellow, IEEE,

and Lina, J. K., Fellow, IEEE, “A Distributed Canny

Edge Detector algorithm and FPGA

Implementation”, IEEE transactions.

[13] Qian Xu, Lina, Karam J, “A Distributed Canny Edge

Detector: Algorithm and FPGA Implementation”,

IEEE Transactions on Image Processing DOI

10.1109/TIP.2014.2311656.

[15] Rao D.V, and Venkatesan M, “An efficient

reconfigurable architecture and implementation of

edge detection algorithm using handle-C,” in Proc.

IEEE Conf. ITCC, vol. 2. Apr. 2004, pp. 843–847.

[16] Varadarajan, S., Chakrabarti, C., Karam, L.J., and

Bauza, J.M., “A distributed psycho-visually

motivated Canny edge detector,”IEEE ICASSP, pp.

822 –825, Mar. 2010.

[17] Wenhao, H., and Kui, Y., “An Improved Canny

Edge Detector and its Realization on FPGA”

IEEE Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing,

China, June 25 - 27, 2008, pp. 6561-6564.

[18] ftp://ftp.altera.com/up/pub/Webdocs/DE2_UserMan

ual.pdf

[19] ftp://ftp.altera.com/up/pub/University_Program_IP_

Cores/VGA.pdf

[20] http://uwf.edu/bshaer/EEL4713L/tut_quartus_intro_

vhdl.pdf


21

VLSI IMPLEMENTATION FOR IMAGING BASED CLASSIFICATION OF

NEURODEGENERATIVE DISEASES- A proposal Neha Gopal. N

1, T. Christy Bobby

2

1,2East Point College of Engineering and Technology, VTU, Bangalore, India

ABSTRACT

Neurodegenerative diseases are mainly occurs due to the deterioration of myelin sheath of neurons, brain spinal cord, and peripheral nerves. The economic and social burden of the diseases is massive and rising too rapidly. There are several kinds of neurodegenerative diseases in that the present work is focused Alzheimer’s diseases. The aim of this work is to automatically classify patients as probable Alzheimer’s diseases (AD) subjects or normal subjects. In this work a fusion strategy is proposed to mix together bottom-up and top-down information flows. Bottom up stage includes a multiscale analysis of different image features, top-down flow includes learning and a fusion strategy which is formulated as max-margin multiple kernel optimization problems.

I. INTRODUCTION

Neurodegenerative diseases are a debilitating condition

where progressive degeneration or death of nerve cells

takes place which causes problems in mental functioning, or

with movement. It basically affects neurons of human body;

neurons are the building blocks of nervous system that includes brain, spinal cord, peripheral nerves. Neurons

neither reproduce nor replace themselves so once when they

are damaged or die they cannot be replaced by human body.

Neurodegenerative diseases comprise variety of mental

symptoms which cannot be evolved by the visual analysis

made by radiologists. World wide it is estimated that

approximately 20-30 million people suffer from

neurodegenerative diseases.Many researchers have

suggested that neuroimaging [1] may become one of the

valuable tool in the early detection and diagnosis of

neurodegenerative diseases. Biochemical, clinical,

neuropsychological analysis against neuroimaging remains to be demonstrated for large population, but still there exists

sufficient evidence of patients suffering with different states

of neurodegenerative diseases.

II. METHODOLOGY

In this project we take the set of normal and abnormal MR images of the patients and compare them. The dataset

used in this work is MIRIAD(Minimal Interval Resonance

Imaging in Alzheimer’s Diseases).The entire dataset is

trained in traning phase; this dataset will calculate the

saliency information from the patients MR images, the

selected features include edges, intensity, orientation.

Support Vector Machine is one of the most popular

technique that is used in this work which will classify

individuals with several neurological disorders. By using

neuroimaging data [7]( MR brain images) a complete

review and comparision of SVM based approaches for classifying neurological and psychiatric diseases can be

made. Features like binary tissue [5] segmentation, cortical

thickness estimations, intensity [2], textural information

[3],[4] is fed to SVM classifier. The computational time, the

presence of unwanted, irrelevant and noisy features is

reduced by using dimensionality reduction technique. The required information for classification is extracted either

from specific regions of interest (ROI) [6]or from whole

brain volume. Analysis which is performed on known

diseases locations leads to more significant and stronger

conclusions. In this work a fusion strategy is used that will

together bottom-up and top-down information flows.

Bottom up stage includes a multiscale analysis of different

image features, top-down flow includes learning and fusion

strategies which are formulated as max-margin multiple

kernel optimization problem.This proposed system is

implemented in MatLab and the module is then

implemented in VLSI domain. The project detailed information is provided in architecture of the proposed

system presented in fig.1

Fig.1 block diagram of the proposed approach

III. Result

Fig2. and fig3. Represents intermediate results of the

current work. The bright region obtained from using visual

saliency technique in the above fig 2 and fig 3 represents

the tumor region in Alzheimer’s diseases and also it gives

information about the size of the tumor and severity the

diseases condition.

[email protected]


22

Fig.2

Fig3. Original MR image and saliency of original image

Conclusion This work is intended to design an automated classification

system for the pathological diagnostic of the disease (AD)

in its infancy stage so that people could be treated before it

becomes to worse over time.

Acknowledgment We acknowledge VGST by DST for funding this project.

References [1] J. Beutel, H. Kundel, and R. Van Metter, Handbook of Medical

Imaging. Bellingham, WA:SPIE Press, 2000, vol.1, Psychophys.

[2] P. Padilla, M. lopez, J. Gorriz, J. Ramirez, D. Salas-Gonazalez, and I.

Alvarez, “NMF-SVM based cad tool applied to functional brain

images for the diagnosis of Alzheimer’s diseases," IEEE

Trans.Med.Imag., vol.31, no.2, pp.207-216, Feb.2012.

[3] M. Garcia-Sebastian, A.Savio, M.Grana, and J. Villanua, “On the use

of morphometry based features for Alzheimer’s diseases detection on

MRI, “in Bio-Inspired System:Computational and Ambeint

Intelligence ser.Lecture Notes In Computer Science. Berlin,

Germany:Springer, 2009, vol.5517, pp.957-964

[4] N. Doan, B.Van Lew, B.Lelieveldt, M. Van Buchem, J. Reiber, and

J. Milles, “Deformation texture-based features for classification in

Alzheimer’s diseases,”SPIE Med.Imag.,2013

[5] M. Liu, D. Zhang, P. Yap, and D. Shen, “Hierarchical ensemble of

multi-level classifier for diagnosis of Alzheimer’s diseases.”in

Machine Learning in Medical Imaging, ser. Lecture Notes in

Computer Sscience. Berlin, Germany:Springer, 2012, vol.7588,

pp.27-35.

[6] B. Magnin et al., “Support Vector machine-based classification of

Alzheimer’s diseases from whole-brain anatomical

MRI,”Neuroradiology, vol.51, pp.73-83, Feb.2009.

[7] G.Orru, W. petterson-Yeo, A. Marquand, G. Sartori, and A. mechelli,

“Using Support Vector Machine To Identify Imaging Biomakers Of

Neurological And Psychiatric Diseases: Article review,

“Neurosic.Biobehav.Rev.,vol.36, no.4, pp.1140-1152, Apr.2012


23

REAL TIME pH MEASUREMENT FOR MONITORING EFFLUENT

DIALYSATE IN AN ARTIFICIAL KIDNEY

Hemalatha R.J1, Shaliya.B

2, Saranya.B

3

Assistant Professor1, Students

2,3, Sathyabama University, Chennai

ABSTRACT

Our paper envisages building an online pH measuring system by which the nephrologist can look at the pH values continuously. The safety with the system ensures that pH is maintained almost neutral and does not create

acidosis to the patient. There is no known work or study related to effluent dialysate. We believe that the effluent

dialysate can provide abundant data which can be analyzed under such circumstances of patient not feeling well

post dialysis.

Keywords: pH, Effluent Dialysate

I. INTRODUCTION There are around 150 countries in the

world reported to provide dialysis care with renal

failure. In the developed countries like Japan, USA

and European countries the rate of kidney failure

and dialysis undergoing patients increases gradually

from 1 to 4 % [1]. Even though dialysis is mainly

done for kidney failure patients but all the patients

are not treated with the dialysis. This is done in

patients with end stage kidney failure i.e, with a

loss of about 90% and GFR < 15. Hence dialysis helps in maintaining the body by removing the

waste from blood[2].

Hence under circumstances the

hemodialysis as treatment for end stage renal

failure patients implementing six times weekly

would harm the patient. Along with this ,there are

only few organ donors available for transplantation,

this gave way for developing the alternative

treatment and devices for kidney. Various factors

plays a vital role in the dialysate namely temperature, pH, volume etc. [3]. The temperature

should be set as 37 to 38 degrees,if it is too low it

leads to reduce d diffusion, if it is too high it leads

to hemolysis. subsequently the pH should be 7.0

and the pH less than 7.0 leads to acidicity and the

pH greater than 7.0 leads to alkalinity. Normal pH

of the body is about 7.35 – 7.45. During

Hemodialysis treatments are made based on the

survey of subjects weight and blood pressure.As a

result treatments are made based on symptoms and

side effects.

This concept of real time monitoring is

based on real time and consequent measurement of

the signals from the subject. Parameters available

for online monitoring are: thermal energy balance., dialysate conductivity, urea kinetics and blood

volume (BV) changes .Our paper envisages on monitoring pH in spent or effluent dialysate during hemodialysis for theoretical evaluation of post

dialysis prognosis of the patient.

II. METHODOLOGY

The scope of our paper lies on defining

that effluent dialysate has data and our paper is

designed to monitor one such data ie pH. Presently

the pH and conductivity are measured either in the

lab by sending the sample or by hand-held meter

that are injected by a syringe or pH strips. In this case the repeatability of the pH and conductivity

cannot be assured unless one does the sampling

frequently. Again, real time measurement is not

possible with pH strips .Our paper highlights on

building an online pH measuring system by which

the sister- in charge or the nephrologist can look at

the pH values continuously. The safety with the

system ensures that pH is maintained almost neutral

and can overcome human error or human

negligence of random sampling of the spent

dialysate. In case of End Stage Renal Failure

patient’s, usually prior to dialysis the patient’s

blood would have accumulated electrolytes, toxins

and excess water during the previous 48 hours

which indicates that the patient’s blood is more

acidic or more alkaline with increased toxins and

edema. The pH measurement after dialysis in the

spent dialysate indicates that the effluent is more

acidic or more alkaline, more toxic which is directly

proportional to the patient’s well being. Online

* [email protected]

mailto:[email protected]


24

measurement of pH at regular intervals indicates the

changing values as per the patient’s using

electrolytes, excess water and other toxins, helps

sister-incharge or the nephrologists can look at the

pH values continuously and it ensures that pH is

maintained almost constant and does not create

acidosis or alkalosis to the patient

A. HYDRAULICS USED

The hydraulics is completely conceived

and fabricated in the inhouse lab. This being the

spinal cord of the entire project, care has been taken

to procedure high quality parts that are suitable to

the design. Keeping in mind that effluent dialysate

should have a free flow, the design of the hydraulics

of the project starts with ‘Y' connector [5]. The

output from the hemodialysis machine is from

12mm internal diameter connector. Usually a flexible pipe of 12.5mm internal diameter is

connected to this connector of dialysis machine.

The flexible pipe is taken to the drain inlet while the

connector on the dialysis machine is 6 inches from

the floor level. Our instrument is kept at a height of

3 feet from floor level to facilitate the flow gradient

from the output of dialysis machine to the project

device and then from project device to the actual

drain.

‘Y’ Connector : The ‘Y’-connector has same bore

diameter as that of the dialysis machine output. A

connector is threaded to the stem of the ‘Y’ which

will act as an inlet to the project device. While the

straight arm goes to a single short solenoid, the right

arm of the ‘Y’-connector is connected to a shunting

connector by an adaptor and a reducer.

Fig:2.1 Y Connector

Fig:2.2 T Joint

‘T’ Joint: 'T' joint is the backbone of the hydraulic

system. The reducer which is connected to the

adaptor in the Y connector is threaded to this ‘T’

joint. The output of the ‘T’-joint is mirror image to

its input, ending in a connector that takes the plastic

tube to the drain at the wall. The ‘T’ joint is placed

as inverted ‘T’ in the project device. The inner

diameter of the ‘T’ section on all its 3 opening is

uniformly 28mm. The stem of the ‘T’ has a

threaded nut into which the pH electrode is inserted. The bulb of the glass electrode is placed at 10mm

from the bottom of the inverted ‘T’ section.

B. SOLENOID VALVE

A solenoid valve has two main parts: the solenoid and the valve. The solenoid converts

electrical energy into mechanical energy which, in

turn, opens or closes the valve mechanically. The

design of a basic valve. At the top the valve in its

closed state. The function of the spring is irrelevant

for now as the valve would stay closed even without

it. The solenoid at the outlet port of the ‘T’-joint is

closed first. After the lapse of a preset time the

solenoid at the inlet port of the ‘T’-joint is closed.

Both the valves are in closed position hence holding

a prescribed volume of fluid in the ‘T’-joint. During this period, the dialysate is flowing freely from the

bypass and the shunt without creating any

backpressure to the effluent dialysis from the

dialysis machine.[6].

The pH electrode in the ‘T’-joint is activated

and a reading of the pH for that particular volume is

noted. The relay that has activated the pH meter

comes to ‘OFF’ state in conjunction with the other two relays that have activated the two solenoid

valves. The above cycle is repeated at preset

intervals for the entire duration of the dialysis

procedure.

The solenoids that store the effluent dialysate in

the pH measuring well are driven by two relays that

operate sequentially to the preprogrammed timing.

In other words, relay 1 which operates the solenoid valve 1 (at the exit of the measuring well) is turned

‘ON’ and relay 2 operates solenoid valve 2 (at the

entrance of the measuring well) is turned ‘ON’.

After a lapse of predetermined time, this

program ensures that the measuring well is filled

and stable during the measuring process.After SV1

and SV2 are closed, relay 3 takes the pH measurement from standby to ‘ON’ state. pH


25

measurement takes few seconds to indicate a

stabilized pH reading. The reading is noted down.

After completing the pH reading, all the three relays

return to their original state.

C. pH METER:

The pH electrode is fixed mechanically to a

threaded cap for facilitating to be immersed into the measuring well. A good feature is it is the addition of a temperature sensor to the pH electrode. By housing the temperature sensor in the same body as the reference elements of the electrode, temperature compensated readings can easily be made with a single pH probe. The meter circuit is no

more than a voltmeter that displays measurements

in pH units instead of volts.. After calibrating the

pH electrode, it is ready for measuring the pH of

other solutions. The pH probe measures pH as the

activity of hydrogen ions surrounding the glass bulb

at its tip. The probe produces a small voltage (about

0.06 volt per pH unit) that is measured and displayed as pH units by the meter.The manual

switch which brings the standby to pH measurement

for calibration purpose also comes with three

connections. In piano switch each of these

connections are taken to appropriate poles so that

the normal position pH measurement is controlled

by the relay [7].

TABLE 1: PH AND TEMPERATURE VALUES OF PATIENTS

The pH value given in the tabular column is

averaged value measured at 20 mins interval during

the dialysis procedure for 240 minutes.

III. RESULT

The pH electrode is calibrated in the

known solutions of 4 pH and 7 pH everyday before

connecting the device to the dialysis machine. The

device is switched ON during the priming of

dialysis machine with a dialysate. At the time of

priming the artificial kidney, the digital timer in the

device is programmed to measure pH of the effluent

dialysate at pre-fixed intervals such as 5 minutes, 3

minutes and so on. At the time of hooking the

patient to the artificial kidney the timer switch for

‘RUN’ is activated momentarily. The timer display

then will show as RUN. After ensuring that

everything is in place, the device is started by

activating ‘START’ button momentarily. The

AUTO/CAL button is returned to AUTO from its

CAL mode. The device is ready to take readings and the readings are noted down in the following

table.Consent of the patient is required only for

putting his name, age, sex and present health status.

IV. SUMMARY AND CONCLUSION

The entire paper represents the hypothesis of the

effluent dialysate that will have useful data.This

data represents invariable the post dialysis of the

patient or the deficiencies in the dialysis itself.The

Dialysis machine has two major subsystems such as

fresh prepared dialysate and artificial kidney. The

dialysate is prepared using pure water (reverse

osmosis water) with known amounts of salts

dissolved into premeasured pure water. The freshly

prepared dialysate is pumped into the artificial

kidney passing around the fibers. The patient’s blood (impure) is drawn into the fibers from one

end and it travels through the other end during

which the impure blood gets purified. During this

process the freshly prepared dialysate becomes

effluent dialysate.

We have divided the paper into three parts

such as hydraulics, a digital timer and the pH meter.

We have bought together all these parts as one integrated system. This system is easy to hook up to

the dialysis machine with simple tube connecting

output of dialysis machine to the inlet of the device.

The device is powered by 230V, 15 Hz mains

supply. The device can be utilised with any make of

hemodialysis machine.. The ease of connecting the

device to the dialysis machine and priming it has

been smooth and with out any drawbacks. It can be

said that the device is ready for further clinical

trails.

The study indicates possible variations and the mean of variations is given to the nephrologist

for verification of the patients’ well being post-

dialysis.It is suggested that the patients’ whole

blood pH may be recorded prior to dialysis and

post-dialysis for further study.If the mean pH of the

dialysate is found to be acidic / alkaline the

Patient Gender pH

value

Tempe

rature

Remarks

Patient

1

M 6.4 37 stable

Patient

2

M 6.5 36 stable

Patient

3

F 6.3 36 stable

Patient

4

M 6.62 37 stable

Patient 5

M 6.5 37 stable

http://www.all-about-ph.com/pt1000-temperature.html

http://www.all-about-ph.com/ph-temperature.html

http://en.wikipedia.org/wiki/Voltmeter

http://en.wikipedia.org/wiki/Hydrogen_ion


26

patients’ post-dialysis blood may become necessary

for bringing it to normal value with appropriate

medications.There are instances when a patient

complains of ‘NOT FEELING WELL’, then the

nephrologist can take a note of the effluent dialysate

variation on the graph along with patients’ post-

dialysis pH value. This can prescribe appropriate

medication to switch the state of the patient to the

condition of ‘FEELING WELL’.The device can be

incorporated in the dialysis machine by its manufacturer for ease of correlating the

readings.Clinically the readings may help during

CRRT extensively if found suitable.

References [1] Kloppenburg WD, Stegeman CA, Hooyschuur

M, van der Ven J, de Jong PE, Huisman RM.

Assessing dialysis adequacy and dietary intake in

the individual hemodialysis patient. Kidney Int

1999; 55:1961-1969.

[2] Brimble KS, St Onge J, Treleaven DJ, Carlisle

EJ. Comparison of volume of blood processed on

haemodialysis adequacy measurement sessions vs

regular non-adequacy sessions. Nephrol Dial

Transplant 2002; 17:1470-1474.

[3] Eddy CV, Arnold MA.Near-infrared

spectroscopy for measuring urea in hemodialysis

fluids. Clin Chem 2001; 47:1279-1286.

[4] Eddy CV, Flanigan M, Arnold MA. Near-

infrared spectroscopic measurement of urea in

dialysate samples collected during hemodialysis

treatments. Appl Spectrosc 2003; 57:1230-1235. [5] Elaine N. Marieb,Human Anatomy and

Physiology,Pearson Education, Dorling

Kindersley,1989,997-1001

[6] Eli A. Friedman, Mary C. Mallappallil, Present

and Future Therapies for End-Stage Renal

Disease,World Scientific Publishing,2010,27-29.

[7] Angel L. M. DE FRANCISCO, Celestino

PIÑERA, Challenges and future of renal

replacement therapy, Christopher R. Blagg,

International Society for Hemodialysis, Blackwell

Publishing Ltd, PIÑERA, 2006.


27

SPATIAL DOMAIN FILTERS IN PREPROCESSING OF OPTICAL

COHERENCE TOMOGRAPHY

Arati Sinha1, Jintu Das

2,K.Venkataraman

3

1,2Department of Bio-Medical Engineering, Bharath University, Chennai. 3Assistant Professor,Department of Bio-Medical Engineering,Bharath University,Chennai.

ABSTRACT Imaging plays an important role in the field of diagnosis. Therefore it is necessary to have images with high resolution and reduced noise. Optical Coherence Tomography images are most commonly affected by speckle noise.There are various methodologies used for reducing this noise. In this paper filtering technique in spatial domain are evaluated to reduce speckle noise in Optical coherence tomography images which also effective for other imaging modalities like ultrasonography where speckle noises are prominent. The performance of these filters were evaluated visually and also based on three parameters namely mean square error (MSE), signal to noise ratio (SNR), peak signal to noise ratio (PSNR). Out of various filters used wiener filters were found comparatively effective. Most filters just average the images instead of removing noise.

Keywords: Speckle noise, Resolution, Mean Square Error, Signal to noise ratio, Peak Signal to noise ratio

1. INTRODUCTION

Optical coherence tomography (OCT) is evolving as a new and promising non-

invasive imaging modality. Ophthalmology

has most widely benefited from inventions and improvements made to OCT imaging. Retinal

images are taken and measurements are

performed on them for diagnosis of pathologies in retinal layers by physicians.

One example for such measurements is

determining the thickness of a single layer of

retina.Other than its primary application in OCT; it is also popular in other medical

imaging tools such as ultrasonography,

gastroenterology, dermatology, endoscopy, urology, brain morphology. The short

temporal coherence property of broadband

light is used by OCT imaging modality to extract structural information from

heterogeneous samples such as tissue. The

main advantage of OCT is that it is non-

invasive, contact-free and gives high resolution both in depth and transversally. But

it has limited penetration in scattering media.

The most severe problem faced in OCT imaging is that the images are corrupted by

speckle noise which complicates the task

ofinterpretation and diagnosis. The main

objective of image denoising is to remove this this type of noise such that the important

features of the signals are retained as much as

possible [1]-[5].

1.1 SPECKLE NOISE

Random variation of image intensity

appearing as grains called as noise. In other

words noise means pixels in the image shows different pixel values instead of original pixel

values. It is the undesirable effect in the image.

Several factors are responsible for introduction of noise in the image during image acquisition

or transmission. The noise can affect the image

to different extents depending on the type of disturbance such as interference in the

transmission channel, insufficient light

levels,sensor temperature etc. Image noise can

be classified as impulse noise (salt and pepper noise), amplifier noise (gaussian noise),

periodic noise, multiplicative noise (speckle

noise), quantization noise [6]. Generally our focus is to reduce speckle noise from OCT

images.

OCT images and other imaging

modalities that involve coherent light source

are affected by speckle noise. Constructive and

destructive interferences of the backscattered waves appear as grains in the image that

degrades the image quality. Physical

parameters of the imaging modality such as size and temporal coherence of light source

and the aperture of the detector also influence

the formation of speckle noise. Speckle is not

purely noise, it also transports image information. So there is difference between

speckle and speckle noise where the later is

pure unwanted component in OCT image signal. Speckle noise reduces contrast and

makes difficult for the highly scattering


28

structures in tissue difficult to resolve. Speckle

is image information and its pattern does not change until there is change in physical

parameters of the imaging system. Speckle and

thus noise are closely correlated. If the entire

speckle in an image of dense tissue is removed completely then no image would remain [6].

2. RELATED WORKS

The standard and most commonly

used filters for speckle noise reduction are adaptive filters which include Lee filter, Frost

filter and Kuan filter which uses multiplicative

noise model. For OCT images, the Rotating kernel transform filter is one of the best single-

resolution filters to reduce speckle noise. The

RKT technique filters an image with a set of kernels and keeps the largest output of the

filter at each pixel. RKT technique is efficient

but do not use any information of the speckle statistics. Noise reduction technique can be

applied before or after formation of image.

The techniques that are applied to the OCT interference signal that is after OCT image is

formed is called post processing techniques.

Other techniques are applied to the complex interference signal, called as complex domain

method. Techniques for despeckling can also be classified into single-resolution and multi-

resolution techniques, such as wavelet-based

techniques. Two single-resolution filters: Lee and RKT filters and four multi-resolution

filters: OCTWT (Wavelet thresholding for

OCT), FBT(Feature based Wavelet Thresholding), WGE (Wavelet shrinkage with

Gamma-Exponential method), BLS-GSM

(Bayesian Least Square estimator with Gaussian Scale Mixture prior) were used on

noise models and evaluated. The performance

of RKT filter was better than the Lee filter but both of the single-resolution filter’s efficiency

was inferior to the performance of

multiresolution images [1].

Reduction of speckle noise using a

partially spatially coherent source was performed in a Michelson interferometer in a

parallel detecting OCT system. A Gaussian-

Schell model for a partially spatially coherent source is used in the OCT stimulation. Such

source was generated by a spatially coherent

broadband light source and a multimedia fiber.

Large number of photons per coherence volume can be produced when multimedia

fiber is combined with broadband light source.

To illustrate speckle reduction with a partially spatially coherent source, low-coherence

interferograms of a scattering surface using

single-mode and multimode source fibers was recorded. There is no degradation in SNR and

no measurable mode cross-coupling is

observed in the multimedia fiber. A partially spatially coherent source is preferable not only

for reducing speckle noise but also for

eliminating airy rings in the image caused by the small field diameter of most single-mode

fibers. Superstition of coherent modes reduces

speckle noise by averaging because each mode forms a statistically independent speckle field.

The results of both simulations and

experiments indicatethat broadband light sources with reduced spatial coherence that

provide a large number of photons per

coherence volume may be effectively utilized to reduce speckle in OCT interferograms.

Image needs to be denoised before processing

them for further analysis [2]-[4].

The various type of noise removing

filters evaluated in [5] are mean filter, median filter, order statistic filter and adaptive filter

(BM3D). BM3D and median filters performed

well in removing speckle noise while averaging filters (mean and median filters) are

not efficient in removing speckle noise. BM3D

is the best filter for removing salt and pepper

noise. Wavelet denoising of multi-frame OCT uses the single capture as input instead of the

average of multiple frames as is common in

OCT processing today. The single frames are wavelet transformed and weights are

computed on transformed data. [6] proposed

two different weights. A significant weight

that determines if noise is locally present and a correlation weight thatdetermines if structure

is present within a local neighbourhood. A

combination of these weights is also possible. The wavelet detail coefficients are scaled with

the weights, averaged and transformed back.

This method removes noise more than the median filter, without degrading the structure

of the image [5]-[7].


29

[8],[10] proposed that filter

incorporates the advantages of median filter in structure retention and adaptability of Lee or

Frost filter to the local speckle and scene

statistics and is simple and effective like Lee sigma filter for reduction of speckle noise. It

computes simple statistics such as mean,

median, standard deviation. It then determines the speckle noise based on local mean and

standard deviation. Then central speckle noise

is then removed and valid pixels keep their original values. Local adaptive median filter

was compared with other adaptive filters and

the lee sigma filter and it was found to be more effective them in speckle noise reduction and

excellent capability of preserving edges and

details. Classical NL-means filter is most suitable for US imaging [8]-[10].

The method of partial differential equations (PDEs) in image significantly

reduce noise with good preservation of edges.

But PDEs such as SOPDE are good for reducing only Gaussian noise and not speckle

noise. For speckle noise reduction, this method

fails to preserve edges. Hence fourth-order PDEs are combined with various speckle

reduction filters such as SRAD filter, Kaun

filter and Lee filter. Gaussian filters can also reduce speckle noise but it do not preserve the

edges and details well. Neural network

perform non linear processing and construct a desirable input and output system by learning.

It is an important component of pattern

recognition and used in the same and in signal processing in various fields. This functions

recognise the noise and judges the area that a medical doctor dose at the diagnosis.[11]-[12].

Speckle denoising method called Volume-BM3D improves OCT image quality

with OCT volume data. This method improves

the SNR of the image of human fingertip efficiently and outperforms other denoising

methods. This method is a powerful tool in

image enhancement applications [13]. A despeckling technique based on multiple

image reconstruction and selective 3-

dimentional filtering was proposed in [14].

Another method of de-speckling of

SAR images in which at first, smoothing of the

coefficients of the highest wavelet sub-bands

is applied on decomposed wavelet coefficients. A Gaussian low pass filter is used for

decomposing the image. Then, the learning of

a Kohonen self-organising map (SOM) is performed directly on the de-noised image to

remove blur. This method is highly useful in

reducing speckle noise in SAR images. However the whole algorithm is

computationally expensive [15]-[16].

In transform domain techniques firstly

the image is transformed into frequency

domain than it will transform in to spatial domain and then domain specific properties

are exploited to process the image. Different

filtering methodologies like fourier transform,

wavelet transform, wavelet domain noise filtering and thresholding are used. All this

methods have some disadvantages. Wavelet

transform techniques are now used to overcome all this disadvantages. RPCA-based

technique was proposed for the first time in

[18] to reduce speckle noise from OCT images. The decomposition of the OCT image

matrix into a sparse matrix of speckle noise

and a low-rank matrix of de-noised image is a

unique feature of this method which can not only reduce speckle noise but also preserves

the structural information of imaged object.

This method is applied to the post-processing of OCT images to enhance the image quality

[17]-[18].

3. METHODOLOGY

The de-speckling filters are evaluated for efficiency in removing speckle noise from

noisy OCT images. The filters evaluated in

this paper are Mean filter [5], Median filter [5],[8], Frost filter [8], wiener filter[10]-[12],

Anisotropic diffusion filter [18]. These filters

are describe below:

3.1 Mean Filter

Mean filter is one of the widely used

low pass linear filter. It effectively smoothens

the image but is has the disadvantage of smearing the edges and fine features together

with the speckle noise, thus fails to preserve

the image structure. The filter computes the


30

average value of the noisy image in a moving

window. Then the center pixel intensity value is replaced by that average value. This process

id repeated for all pixels in the.

3.2 Median filter

The median filter removes pulse and

spike noise successfully while retaining ramp and step function. Therefore median filter is

better than mean filter in terms of preserving

edges between two different features but it doesn’t preserve single pixel-wide features,

which will be altered if speckle noise is

present.The 3*3 median filterpreserves the texture informationbut it doesn’t give the mean

value. The use of mean and filter is limited in

speckle noise reduction because of the

multiplicative nature of the speckle noise which means that amount of noise is

proportional to the intensity of the signal.

Mean and median filters are not adaptive filters. They do not consider any particular

speckle properties of the image.

3.3 Frost filter

The frost filter replaces the pixels of

interest by weighted sum of the values of all pixels within the moving window. This filter

was developed by Frost et al., in 1981-1982.

The weighting factors decreases with distance from the pixel of interest and increases for the

central pixels as variance within the window

increases. Multiplicative noise and stationary noise statistics are assumed by this type of

filter.

3.4 Anisotropic diffusion filter

Diffusion means balancing the

difference in concentration of an image.

Anisotropic diffusion is a generalization of this diffusion process. It performs two diffusion

processes: edge-enhancing anisotropic

diffusion and coherence-enhancing anisotropic diffusion. Anisotropic diffusion filter extracts

a family of multi-resulution derived images in

order to identify global objects through blurring. These filters are mostly used in

segmentation and edge detection, as they are

good in preserving edges while smoothing the

other regions of the image.

3.5 Wiener filter

There are two methods for

implementing wiener filter : Fourier-transform

method (frequency-domain) and mean-squared error (spatial-domain). The former method is

used for denoising and deblurring and the later

method is used only for denoising. In Fourier transform method of Wiener filtering,

normally it requires a priori knowledge of the

power spectra of noise and the original image. But in mean-squared method no such a priori

knowledge is required. Hence, it is easier to

use the mean-squared method for image

denoising. Wiener filter works on the principle of least-square, i.e., the filter minimizes the

MSE between the actual output and the desired

output. Wiener filter assumes both global and local statistics as image statistics vary largely

from one region to another within the same

image. Wiener filter estimates the original data with minimum MSE and hence the noise

power in the filtered output is least.

These filters are evaluated quantitatively in terms of Mean Squared Error (MSE), Signal

to noise ratio (SNR), Peak signal to noise ratio

(PSNR), correlation, homogeneity and Energy. In addition to these quantitative measures,

qualitative visual inspection is also used to

compare results of various filters mentioned above. Fig. 1 below shows an OCT image

affected with speckle noise and its

monochromatic image.


31

Fig 1.OCT image with speckle noise (colour and monochromatic)

4. RESULTS

The outputs of the various filters used on speckle affected OCT images are shown in figure 2 below:


32

Fig 2: Output of various speckle reduction filters. (a) Mean filter 3*3, (b) Mean filter 5*5, (c) Mean

filter 7*7, (d) Median filter 3*3, (e) Median filter 5*5, (f) Median filter 7*7, (g) Wiener filter 3*3, (h) Wiener filter 5*5, (i) Wiener filter 7*7(j) Anisotropic filter (k) Frost filter

After filtering the noisy images, quantitative statistics were calculated. The MSE, SNR and PSNR of the filters are shown in table 1.

Table 1. Performance factors of various filters

Filter MSE SNR PSNR Correlation Homogeneity Energy

Mean (3*3) 0.0038 76.6442 76.6765 0.9674 0.889 0.1879

Mean (5*5) 0.0076 69.9243 69.2546 0.983 0.9343 0.2188

Mean (7*7) 0.0100 68.7523 68.7645 0.9883 0.9543 0.2329

Median(3*3) 0.0101 65.7553 65.6474 0.9844 0.899 0.1993

Median (5*5) 0.0102 65.7776 65.8753 0.9848 0.9212 0.2044

Median (7*7) 0.0110 65.8753 65.9836 0.9901 0.9311 0.2143

Anisotropic 0.0075 69.8882 69.2785 0.924 0.914 0.203

Frost 0.0034 77.6671 76.64532 0.97 0.88 0.199

Wiener(5*5) 1.1*10-7

95 95 0.98 0.94 0.225

As seen in the table and plots, wiener

filter shows comparatively greater value of SNR and PSNR value than other filters. So

comparatively it is a better speckle removing

filter than other filters evaluated here. In addition to this quantitative measure,

qualitative visual inspection is also used to

compare results of the different filters evaluated. Mean and Median filters just

averaged the image. Frost filter removed the

speckle noise. However it was unsuccessful in preserving the edges and fine details.

Anisotropic diffusion filter de-speckled the

noisy image quite effectively, but poorer than wiener filter as it blurs the image.

Not restricting to Error and SNR, also taking correlation, homogeneity and energy

into consideration, it could be observed from

the table that Wiener filter seems to be optimistic by all means. It could be seen that

the wiener filter not only reduces the speckle

noise but also preserves required edge information thereby easing the process of

further analysis and classification.

5. CONCLUSION

Presence of speckle noise as grainy

salt-and-pepper patterns in OCT images

degrades the image quality and further

complicates the process of future tasks. In order to take full advantage of the OCT

imaging in the field of ophthalmology and

other imaging modalities for disease diagnosis, speckle noise has to be removed. In this paper

we evaluated some of the spatial domain

speckle reduction filters for OCT images. During the experiments, quantitative measures

like MSE, SNR, PSNR, homogeneity,

correlation, and energy were used to compare the denoising capability of various filters. Both

quantitative and visual inspection shows that

wiener filters were comparatively more effective in reducing speckle noise and also

successful in preserving the edges. Mean and

median filters performed worst. Frost and anisotropic diffusion filter reduced speckle

noise but could not preserve the edges and fine

details well. The result also indicates that different filters have different advantages over

another and the choice of particular filter may

depend on the type of use (Eg. enhancement for visual inspection or preprocessing for

segmentation of most prominent features in the

image). Most of the filters have smoothened the image with blurring effect.


33

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3, No. 6.

34

IMPULSE NOISE REDUCTION FROM MAMMOGRAM IMAGES

USING NOVEL KT FILTERS

R. R. Subash Chandra Boss1, K. Thangavel

2

1,2 Department of Computer Science,

Periyar University, Salem

Abstract

One of the vital challenges in the field of image processing and computer vision is image denoising,

where the essential objective is to estimate the original image by suppressing noise from a noise-contaminated

version of the image. Image noise may be caused by different intrinsic (i.e., sensor) and extrinsic (i.e.,

environment) conditions which are often not possible to avoid in practical situations. Therefore, image

denoising plays an important role in a wide range of applications such as image restoration, visual tracking, image registration, image segmentation, and image classification, where obtaining the original image content is

crucial for strong performance. While many algorithms have been proposed for the purpose of image denoising,

the problem of image noise suppression remains an open challenge, especially in situations where the images are

acquired under poor conditions where the noise level is very high. This paper proposes different filtering

techniques based on frequency methods for the removal of impulse noise. The proposed filtering model is

validated by experimental analysis. In this study, the statistical quantity measures such as Signal-to-Noise Ratio

(SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) are used to measure the

quality of enhanced images.

Keywords: Mammogram, SWT, Pre-processing, Impulse noise, PSNR, SNR and RMSE

1. Introduction

Breast cancer cases are spiraling world

over, and urban India is no exception. India’s

National Health Profile 2010 predicts that by 2020,

breast cancer will overtake cervical cancer as the

most common type of cancer among women in

India. Dr Rajni Mutneja, head of preventive

oncology at Rajiv Gandhi Cancer Institute, Delhi,

stated that almost one in 20 women in metropolitan cities are suffering from breast cancer and cases

have almost doubled in the last decade [1]. Indian

Council for Medical Research (ICMR) reported

that breast cancer has nearly doubled in the last 24

years. As per the International Agency for Research

on Cancer (IARC), India could see around 250,000

new cases of breast cancer by 2015 [2].

Medical imaging is the technique and

process used to create images of the human body

for clinical purposes (medical procedures seeking to reveal, diagnose, or examine disease) or medical

science (including the study of normal anatomy and

physiology). It is widely acclaimed as a hallmark of

modern medicine. Diagnostic imaging is an

umbrella term for a wide variety of scans,

examinations and images that are used in the field

of medicine such as X-ray, Computed Tomography

(CT), Magnetic Resonance Imaging (MRI),

Ultrasound (US) and Dermoscopy Images (DI) [3].

There is no doubt that medical imaging solution

technology plays a vital role in the diagnosis and

treatment of patients suffering from serious illness.

Unfortunately, medical images encounter a various

number of noises such as Gaussian, Poisson, Rician

and impulse noise (salt and pepper noise) [4]. This

study focuses on impulse noise as medical images

are mainly prone to this type of noise [5].

Digital mammograms are medical images

that are complicated to be interpreted, thus a preparation phase is needed in order to improve the

image quality and make the segmentation results

more accurate. The main objective of the digital

image pre-processing step is to improve the quality

of the image to make it ready to further processing

by removing the isolated and surplus parts in the

back ground of the mammogram [6, 5].

This paper is organized as follows: Section

2 describes the related work. Section 3 discusses

the Stationary Wavelet Transform. Section 4 explains the median filter techniques and M3 filter

techniques. Section 5 discusses the proposed

methods. Section 6 discusses experimental results

and Section 7 covers conclusion.

2. Related work

Impulse noise is frequently encountered in

acquisition, transmission, storage and processing of

images. The presence of impulse noise in an image

may be either relatively high or low. Thus, it could

severely degrade the image quality and cause some loss of image information details. Filtering a digital

image to remove noise and keeping the image

details is an essential part of this task. Various

35

filtering techniques have been proposed for

removing impulse noise in the literature: The

wavelet transform has become an important tool for

this problem due to its energy compaction property

[7]. Indeed, wavelets provide a framework for

signal decomposition in the form of a sequence of signals known as approximation signals with

decreasing resolution supplemented by a

sequence of additional touches called details

[8,9]. Denoising or estimation of functions,

involves reconstituting the signal as well as

possible on the basis of the observations of a

useful signal corrupted by noise [10-13]. The

methods based on wavelet representations yield

very simple algorithms that are often more

powerful and easy to work with than traditional

methods of function estimation [14].

Wang L et. al, proposed wavelet

filters[15,16]. Osman et. al., presented the image

noises are eliminated using the Stationary Wavelet

Transform, SWT, with time invariant characteristic

which is particularly useful in image denoising

[17]. Hasan Demirel et al., proposed an image

resolution enhancement technique based on

interpolation of the high frequency subband images

obtained by discrete wavelet transform (DWT) and

the input image. The edges are enhanced by

introducing an intermediate stage by using stationary wavelet transform (SWT) [18].

Hence, many models have been proposed

for noise removal from medical images. While

some of these models use complicated

formulations, others require deep knowledge about

image noise factors. Hence, a simple noise

reduction method that removes noise well and

preserves image details without relying on image

noise factors is desirable. The proposed methods

which are explained in section 5 removes any level

of impulse noise, is applicable for almost all noise models, these models do not use

complicated formulations and not required deep

knowledge on image noise factors.

3. Stationary wavelet method

Wavelet Transform is superior approach

to other time-frequency analysis tools like

Fourier Transform (FT) and Short Term Fourier

Transform (STFT) because its time scales width

of the window can be restricted to match the original signal especially in image processing

applications. This makes that it is particularly

useful for non-stationary signal analysis such as

noises and transients. For discrete signal, DWT

is a Multi-resolution Analysis (MRA) and it is

a non-redundant decomposition. The drawback of

non-redundant transform is their non-variance in

time [19]. The stationary wavelet transform (SWT)

was introduced in 1996 to make the wavelet

decomposition in time invariant [20, 21]. In order

to preserve the invariance by translation, the down-sampling operations must suppressed and

the decomposition obtained is redundant and is

called stationary wavelet transform, which is as

shown in figure 1.

Figure 1. Stationary Wavelet Transform

SWT has similar tree structured

implementation without any sub-sampling. This

balance of Perfect Reconstruction (PR) is

preserved through level dependent zero padding

interpolation of respective low pass and high

pass filters in the filter bank structure. SWT has

equal length of wavelet coefficients at each level.

The computational complexity of SWT is high as

compared to Discrete Wavelet Transform and need

larger storage space. And which is represented as O (n2). The redundant representation of SWT makes

shift-invariant and suitable for applications such

as edge detection, de-noising and data fusion

[22]. In stationary wavelet transform (SWT)

instead of down sampling, an up sampling

procedure is carried out before we separate the

variables x and y of image f (x, y) shown in the

following wavelets:

Vertical wavelet

(LH):

)()(),(1 yxyx

Horizontal wavelet (HL):

)()(),(2 yxyx

Diagonal wavelet (HH):

)()(),(3 yxyx

36

x+1, y

x-1, y-1 x, y-1 x+1, y-1

x-1, y x, y

x-1, y+1 x, y+1 x+1, y+1

wavelet is function and is the scaling

function. The detailed signals contained in the three

sub-images are as follows:

ly

kjlx

yxj lylxclyhlxgkkw j ),()()(),(2,

1

1

ly

kjlx


2

1

ly

kjlx


3

1

4. Median filter

It is possible to filter out the noise present

in image using filtering. A high pass filter

passes the frequent changes in the gray level

and a low pass filter reduces the frequent

changes in the gray level of an image. That is the

low pass filter smoothes and often removes the sharp edges. A special type of low pass filter is the

Median filter. The Median filter takes an area

of image (3 x 3, 5 x 5, 7 x 7 etc), observes all

pixel values in that area and puts it into the array

called element array. Then, the element array is

sorted and the median value of the element

array is found out. It has been achieved this by

sorting the array in ascending order using Bubble

sort. The middle in the sorted array is taken as the

median. The output image array is the set of all the

median values of the element arrays obtained for all

the pixels. Median filter goes into a series of loops which cover the entire image array [23].

Some of the important features of the

Median filter are: It is a non-linear digital filtering

technique. It works on a monochrome color

image. It reduces “impulse” and “salt and paper”

noise. It is easy to change the size of the Median

filter. It removes noise in image, but adds small

changes in noise-free parts of image. It does not

require convolution. Its edge preserving nature

makes it useful in many cases. The selected median

value will be exactly equal to one of the existing brightness value, so that no round-off error is

involved when we take independently with

integer brightness values comparing to the other

filters [23, 24].

M3- Filter

The M3-Filter is a hybridization of mean

and median filter [25]. This replaces the central

pixel by the maximum value of mean and median

for each sub images SXY. It is expressed as M3-

Filter, the intensity values are reduced in the

adjacent pixel and it preserves the high frequency

components in image. Therefore it may be suitable

for denoising the speckle noise in the ultrasound

medical image. It is a simple, intuitive and easy to

implement method of smoothing images. )}),({)},,({max(),(

),(),(tsgmeantsgmedianyxf

xyxy stssts

5. Kernel Threshold (KT) based Filtering

Techniques

A novel filter called KT filter is proposed

which is the enhancement of M3 filter. M3 filter

considers all the pixels in sub image but in this

method we consider twelve different schemes of

sub images. The figure 3 shows the KT Filter scheme models. This method replaces the central

pixel by the maximum value of mean and median

for each sub images SXY. It is expressed as KT-

Filter, the intensity values are reduced in the

adjacent pixel and it preserves the high frequency

components in image. Therefore it may be suitable

for denoising the impulse noise in the mammogram

medical image. It is a simple, intuitive and easy

method for smoothing images. The figure 2 shows

the Sub-image pixel co-ordination.

Figure 2. Sub-image pixel co-ordination

Scheme Models

KT1 KT2 KT3 KT4

KT5 KT6 KT7 KT8

37

KT9 KT10 KT11 KT12

Figure 3. KT Filter Scheme Models

6. Experimental and results

The proposed methods have been

implemented using MATLAB. The mammogram

images used for experimental analysis are taken

from the Mammographic Image Analysis Society

(MIAS). This corpus consists of 322 images, which

belong to three big categories: normal, benign and

malign. There are 208 normal images, 63 benign

and 51 malign, which are considered abnormal.

The sub-images of size 3 x 3 are used for

constructing KT filter. The important factors for

performance evaluation of the reconstruction algorithm are speed and the quality of the

reconstructed images. While the system cost and

speed are dependent on many factors including the

advancement in technology and the process in

hardware and software implementation, the image

quality is a technology-independent measure.

Image quality can be used to compare the

performance of the different system and select the

appropriate processing algorithm for a given

application. Hence, image quality assessment plays

an important role in image processing application. A great deal of effort has been made in

recent years, and several image quality matrices

have been developed in addition to visual analysis,

to predict the visible different between a pair of

image, the input image I(x,y) and the resultant

image I′(x,y). The figure 4 shows the proposed

system.

Different measures [26, 27] are used to

compare the performance of the filtering methods.

For quantitative evaluation, there common error

measurements, viz., MSE, RMSE, SNR and PSNR

are widely employed [28, 29]. The MSE, RMSE,

SNR and PSNR values for the Median, M3 and KT

Filters overall performance of statistical

measurements are tabulated in table1. It is observed

that the KT1 filter gives the best result for all tested

image. Figure 5 shows that the proposed method

results in images MDB030.

Figure 4. Proposed system

Median PSNR : 39.14461 M3 PSNR : 38.71739 KT1 PSNR : 46.90071 KT2 PSNR : 28.59734

KT3 PSNR : 38.68543 KT4 PSNR 38.69705 KT5 PSNR : 40.49473 KT6 PSNR : 43.86343

Origi

nal

image

KT Filter

Inver

se

Statio

nary

wavel

et

De-

noise

d

image

Statio

nary

wavel

et

38

KT7 PSNR : 35.9684 KT8 PSNR : 38.64685 KT9 PSNR : 34.64424 KT10 PSNR : 34.26017

KT11 PSNR : 33.43632 KT12 PSNR : 34.17703

Figure 5. Proposed method result in image MDB030

Table1. Overall performance of statistical measurements

for MIAS database

7. Conclusion

In this work, KT filter method is proposed as

pre-processing for mammogram images. Mammogram

images are susceptible to impulse noise, which results in inaccurate analysis that can prove

disastrous. Median filters and its variants are commonly

used for impulse noise removal, but they result in loss

of image details. Also, for high noise levels, they are

not sufficient in completely removing the noise. Using

our pre-processing method, noise is eliminated

effectively. The performance of the KT Filter is

evaluated using MSE, RMSE, PSNR and SNR

measures and compared with benchmark Median filter

and M3 filter. It was observed that KT1 Filter out

performs the benchmark Median and M3 filter. Further

the resultant mammogram can be used for accurately

detect region of interest (ROI), abnormalities in human

breast like calcification, circumscribed lesions etc.

S.No Filter MSE RMSE PSNR SNR

1 Median

6.881902 2.559121 39.44869

0.05251

3

2 M3

7.124516 2.622721 39.14855

0.05705

4

3 KT1

1.263565 1.108218 46.59112

0.01632

1

4 KT2

53.83242 7.162768 30.47873

0.04952

2

5 KT3

7.131364 2.623537 39.1469

0.04579

5

6 KT4

7.169801 2.632867 39.10955

0.04423

6

7 KT5

4.245671 2.003048 41.61886

0.03129

9

8 KT6

2.317237 1.49718 43.98814

0.00224

4

9 KT7

14.50898 3.749193 36.03314

0.07207

7

10 KT8 8.115979 2.80445 38.54341

0.066743

11 KT9 18.86642 4.285378 34.83178 0.06562

12 KT10

19.25543 4.332257 34.73163

0.06447

5

13 KT11

26.34454 5.061241 33.40312

0.08659

8

14 KT12

18.25871 4.205617 35.01777

0.08819

9

Statistical

Measurement

Formula

MSE

MN

jiFjif 2)),(),((

RMSE

MN

jiFjif 2)),(),((

SNR 2

2

10log10e

PSNR

RMSE

255log20 10

39

Acknowledgment

The first author immensely acknowledges the

partial financial assistance under University Research

Fellowship, Periyar University, Salem.

The second author immensely acknowledges the UGC, New Delhi for partial financial assistance

under UGC-SAP (DRS) Grant No. F.3-50/2011.

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highly corrupted images. IEEE Trans Image Process 1996;

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Michel Poggi, “Wavelets and their Applications”, Published

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40

DETECTION AND EXTRACTION OF BLOOD VESSEL OF RETINAL IMAGES

IN DIABETIC RETINOPATHY USING FILTERS

Subhra Pattnaik, Asit Subudhi, Sunita Sarangi, Sukanta Sabut* School of Electronics Engineering

Institute of Technical Education & Research SOA University, Bhubaneswar, Odisha

ABSTRACT

Diabetic retinopathy is the result of damage to the tiny blood vessels that nourish the retina. They leak blood and other flu ids that cause swelling of retinal tissue and clouding of vision. The condition usually affects both eyes. The longer a person has diabetes, the more likely they will develop diabetic retinopathy. Diabetic Retinopathy (DR) is a medical condition that affects the microvasculature of the retina which leads to loss normal vision of a human. It is the most common causes of blindness in adult group of both developed and developing countries. Early detection of DR helps the ophthalmologists to advise proper treatment to save the vision of the patient. The change in appearance of retinal blood vessel structures can be detected by using edge detection and matched filter techniques. Segmentation of vascular structures of retina for implementation of Clinical diabetic retinopathy decision making systems is presented in this paper. The Canny edge detector, matched filter and matched filter with first order derivative of Gaussian filters have been implemented to find the abnormalities in diabetic retinal blood vessels and the results were compared by evaluated parameters between the original affected image and the output images of the work. The Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) values are computed for quantitative evaluation of the operators. The simulated result of segmented images shows that the vessel ext raction by MF-FDOG outperforms compared to MF and canny operators in detecting even small blood vessels of retina. The calculated PSNR value is also high for the MF-FDOG than the matched filter and Canny operator, it indicates the better performance of MF-DOG in extraction of blood vessels of DR retinal images.

Keywords: Diabetic retinopathy, retinal images, blood vessels, matched filter, MF-FDOG, Canny edge detector, PSNR, MSE

I. INTRODUCTION Diabetes is a disease that interferes with the body's ability

to use and store sugar, which can cause many health

problems. Diabetic retinopathy (DR) affects human eyes.

The blood vessels in a human are very small in size and

hence more susceptible to DR. The corrosion of blood

vessels starts occurring when the blood sugar levels are increased much above the normal levels for a prolonged

period of time. The Indian Council of Medical Research

estimated that around 65.1 million patients are affected by

diabetes, and by 2030, India will have 101.2 million

diabetic people [1]. Diabetic retinopathy is a vascular

disorder that affects the microvasculature of the retina. It

has been one of the foremost causes of blindness in the

working age group of both developed and developing

countries. About 33% of patients with diabetes have signs

of diabetic retinopathy. It damages the small blood vessel

of the retina, which leads to loss of vision [2]. Current research indicates that at least 90% of new diabetic

retinopathy cases could be monitored properly at the early

stages [3]. Artery-vein crossings and patterns of small

vessels can serve as a diagnostic indicator of diabetic

retinopathy. An accurate delineation of the boundaries of

blood vessels makes precise measurements of these features

that may be applied for diagnosis, treatment evaluation and

clinical study [4]. The retinal blood vessels can be directly

visualized non-invasively with very high resolution for

most clinical scenarios [5]. Edge detection is a common

starting step in many image processing applications. The

edge in an image provides useful structural information

about object boundaries [6]. Edge detection techniques

significantly reduce the amount of data and filter out useless

information while preserving the important structural

properties in an image.2-D Gabor wavelet and supervised

classification was used for automated segmentation of the

vasculature in retinal images by classifying each image

pixel as vessel or non-vessel, based on the pixel‟s feature

vector [7].Vessel extraction is basically a kind of line

detection problem and many methods have been proposed.

A class of popular approaches to vessel segmentation is

filtering based methods [8, 9] which work by maximizing the response to vessel-like structures. Mathematical

morphology [10] is another type of approach by applying

morphological operators. Trace-based methods [11] map-

out the global network of blood vessels after edge detection

by tracing out the center lines of vessels. Such methods rely

heavily on the result of edge detection. Vessel detection and

tracking by matched Gaussian and Kalman filters was used

with second-order derivative of Gaussian matched filter

which was designed according to the known blood vessel

feature, and used to locate the center point and width of a

vessel [12]. Vessel tracking methods are based on the principle of measuring some local image properties to

outline the vessel points which are further used to trace the

retinal vasculature. Feature extraction and recognition of

vessel structure are both performed simultaneously in these

types of algorithms. A new algorithm was presented which

follows matched filtering approach and is instigated by start

and end points followed by automatic detection of vessel


41

boundaries [13]. The classical matched filter (MF) method

has advantages of simplicity and effectiveness among the

various retinal vessel extraction methods, which detects

vessels by filtering and thresholding the original image [8].

Morphological operator was used to extract only the blood

vessel and then the edge of the blood vessels were clearly

detected by applying Laplacian and Gaussian operators

[14]. A multi-scale segmentation function assists in

detecting the retinal vessel structures [15]. The response of

the MF-FDOG method by using both the Matched Filter

(MF) and the First-Order-Derivative of the Gaussian (FDOG) was found to be more accurate in distinguishing

between the retinal vessels‟ structures compared to MF

alone [16]. An improvement in detecting retinal vessel both

qualitatively and quantitatively has been found by the

centre line extraction combining with matched filter

responses [17]. A new technique was proposed where all

together the edge detection, matched filtering and region

growing procedures has been used as a group for the

detection of retinal blood vessels in retinal images [18]. A

novel technique of adaptive local thresholding was

proposed by combining verification-based multithreshold scheme with classification procedures in [19], used for the

detection of vessels in retinal images. A technique used for

the segmentation of blood vessels in retinal images that

combines morphological filters and cross-curvature

evaluation [20]. The blood vessels are extracted from the

retinal images with better PSNR and 96% accuracy by

applying a curvelet transform and curvelet coefficients

[21].

In this paper, we have detected and segmented the

blood vessels by the canny edge detector, matched filter and

matched filter with first derivative of Gaussian (MF-FDOG)

for identifying the structures of the blood vessel in retinal

images, and the results are compared by the evaluating the

quantitative parameters PSNR and MSE between the gray

scaled image and the output image of canny operator and

filters.

II. METHOD

The retinal image is collected from an eye institute with permission from the ophthalmologist. One affected sample image is selected from ten sample retinal images. Blood vessels are extracted for the identification of damages in diabetic retinopathy and the canny operator, matched filter and matched filter with first derivative of Gaussian algorithms are applied to detect the blood vessel structures as per flow diagram. The RGB image is first converted into grayscale to strengthen the appearance of blood vessels. Initially, the original image was scaled to enhance the contrast of the image, and canny edge operators is applied to detect blood vessels by using MATLAB 9.0.Similarly, matched filter kernel is loaded then the original image is converted to gray scale image and Matched filter algorithm

is applied in order to find the threshold and segmented image. Similarly MF-FDOG algorithm is applied to get the segmented and threshold image. The methods of canny, MF and MF-FDOG have been described in segment A, B and C respectively.

Retinal RGB

Image

Gray Scale Image

Enhanced Image

Segmented image by Canny edge, Matched filter, MF-FDOG detection

Extracted Blood Vessel detection

Fig.1. Flow chart of blood vessel extraction of retinal image A. Canny operator

The Canny operator works in a multi-stage process. As it is susceptible to noise present in raw unprocessed image data, the raw images were smoothed out by convolving with a Gaussian filter [22]. Canny uses convolution mask in x and y directions, and after that bring it to compute gradient before a non-maximum suppression process. Computation of the gradient of image f(x, y) is carried out by convolving it with the first derivative of Gaussian masks in x and y directions. Smooth the image with a Gaussian filter to reduce noise and unwanted details and textures. The different processes of canny operator are applied to images and the results were presented in Fig. 2.

g(m,n)= (m,n)*f(m,n) ……………(2.1)

where = exp[-( ) /2 ]

B. Matched Filter

The matched filter is one of the template matching algorithms that are used in the detection of the blood vessels in retinal images and other application as well. The matched filter kernel may be expressed from [8] K(x, y) =

)

…………(2.2)

where L is the length of the vessel segment that has the same orientation, defines the spread of the intensity profile. To be able to detect vessels on all possible orientations, the kernel must be rotated to all possible vessel orientations and the maximum response from the filter bank is registered. Many papers found that rotating by an amount


42

of 15 is adequate to detect vessels with an acceptable amount of accuracy which results a filter bank with 12 kernels. The authors of [8] made some experiments on the values of L and and found that the best parameter values were those that gave the maximum response at L=9 and

=2. A Gaussian curve has infinitely long double sided trails; the trails are truncated at u=±3 .

The neighborhood „N‟ is defined such that

N={(u,v)

………………….(2.3)

The corresponding weights in the kernel i (i=1………12

which is the number of kernels) are given by

ki (x,y)=

/)

∀ Pi∈ N ….......................(2.4)

If A: number of points in N, the mean value of kernel is determined as

mi= ... ..............................(2.5)

Thus, the convolution mask used in this algorithm is given by

K´i(x,y)=ki(x,y)-mi, ∀ Pi ∈ N ……..………(2.6) C. MF-FDOG The matched filter is the zero-mean Gaussian filter. The first derivative of Gaussian is FDOG. According to [16] MF detects both vessels and non-vessels whereas MF-FDOG method is very simple and effective in detecting fine vessels that are missed by the MF. The first-order derivative of the Gaussian (FDOG) is G(x,y)=(- / exp(− / ) …………..(2.7) According to the paper in [16] the MF and the FDOG are

merged to form „„MF-FDOG‟‟ for vessel detection. Signal‟s response to the MF is denoted by h. A threshold T is then

applied to h to detect the vessels. The vessels and non-vessel edges can be better distinguished by thresholding their

responses to MF. The response of the input signal to the FDOG is denoted by d. The local mean of d is then calculated and

denoted by Dm. The local mean value of an element in d is defined as the average of its neighboring elements. In contrast,

in the neighborhood of the step edge there are also strong responses in h but the corresponding responses in Dm are very

high. Therefore, the local mean signal Dm can be used to adjust the threshold T to detect the true vessels while removing

the non-vessel edges. In other words, T should depend on Dm. If the magnitude in Dm is low, this implies that a vessel may

appear in the neighborhood, and hence the threshold T applied to h can be small to detect the vessels; if the magnitude in Dm

is high, this implies that some non-vessel edges may appear, and hence the threshold T can be high to suppress the non-

vessel edges. A thresholding scheme has been applied according to paper [16] by using the MF-FDOG for retinal

vessel detection. The threshold is applied to the retinal image‟s

response to MF but the threshold level is adjusted by the image‟s response to FDOG. After filtering the retinal image

with the MF-FDOG filters, two response images, H (by the MF) and D (by the FDOG) are obtained. The local mean image of D is calculated by filtering D with a mean

filter: Dm=D*W ……………. (2.8) Where W is a filter whose elements are all 1/w

2.The

local mean image is then normalized so that each element is within [0, 1]. The normalized image of Dm is denoted by

m. The threshold T is then set as

T= (1+ m).Tc ……...................(2.9) where Tc is the reference threshold and is defined as:

Tc=c. …………………………. (2.10)

: mean value of the response image H, and c is a constant which is set between 2 and 3 according to the paper [16]. By applying T to h, the final vessel map MH is obtained as: MH=1 H(x,y) T(x,y) ………………(2.11)

MH=0 H(x,y)<T(x,y) ………………(2.12) D. Evaluation Parameter The objective measures the PSNR used to evaluate the intensity changes of an image between the original affected image and the output images. The Peak-Signal-to-Noise Ratio (PSNR) can be computed as PSNR=10 ………….. (3.1)

and the Mean Square Error (MSE) is given by

1m n

2

MSE

Ia (i, j) Io (i, j) ..…… (3.2)

mn i 1 j 1


An affected diabetic retinopathy image was chosen from

ten sample images collected from an eye institute. The images are preprocessed to extract the features in a more clear and proper manner. Hence lots of research has been carried out. The initial pre-processed methods are applied to attain better enhanced images according to the flow diagram is represented in flow diagram as shown in Figure 1. The canny edge operator was used to the affected DR images and the results are shown in Figures 2(a)–2(b). Similarly, the matched filter was applied to the image to get the output and the results are shown in Figures 3(c)-3(e). Then first derivative of Gaussian was applied to the image to get the segmented output and the results are shown in Figures 4(f)-4(h).Figure 5(i) represents the resultant of MF merged with FDOG. From the output images of canny edge detector, match filter and MF-FDOG it was observed that the MF-FDOG works better than MF and Canny edge detectors for detecting small blood vessels. The PSNR and MSE parameters were calculated between the grayscale images and the output images of canny edge detector, MF,MF-FDOG and the result is represented in Table1. The higher the PSNR value the better will be the performance and lower the MSE value better will be the performance hence the simulation result shows that the blood vessels extracted


43

from the retinal images has better PSNR values in the MF-FDOG as compared to MF and Canny operator.

(a) (b) Fig. 2.Simulation results of a diabetic image by canny operator (a) Gray Scale image; (b) Canny Operator.

(c) (d)

(e)

Fig. 3. Simulation results of a diabetic image by matched filter (c) Gray Scale image; (d) matched filtered image; (e) thresholded image at 0.35.

(f) (g)

(h) Fig. 4. Simulation results of an affected image by first derivative of Gaussian (f) gray scale image; (g)FDOG image; (h) thresholded Image at 0.35.

(i)

Fig. 5. (i)Simulation results of an affected image by merging matched filter with first derivative of Gaussian at threshold 0.35. Table 1: The PSNR and MSE results of different operators

Parameters Matched MF-FDOG Canny Filter(MF) Operator

PSNR 58.99 60.52 57.46

MSE 0.091 0.057 0.116

IV. CONCLUSION

Many different techniques could be used for extraction

of blood vessels in DR images. Initially the images are preprocessed to increase the contrast and also to enhance the image so that the vessels can be extracted in a better and efficient manner. In this paper, we have implemented the canny edge detector, matched filter, MF-FDOG filter for extraction of retinal blood vessel. The results were compared for different operators by visualizing the output images and evaluating the quantitative values obtained from input images and extracted output images. The result indicates that all the operators successfully detected the edges of the retinal blood vessels, but the MF-FDOG extraction method performed better compared to MF and canny operator for detecting small blood vessels of the retina in terms of better image quality, high PSNR and low MSE values. Thus, it was concluded that the MF-FDOG is a better choice in detecting the continuous and smooth edges of the blood vessels. It will provide better results for identifying the blockages of blood vessels in the retina for the diagnosis of diabetic retinopathy by ophthalmologists. Further work is necessary to validate the results by classifying the images in classifiers for more number of images.


44

Acknowledgment Authors would like to thank the ophthalmologists of the L.V. Prasad Eye Institute, Bhubaneswar, for providing the retinal images that has been used in this work.

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45

A NOVEL EARLIER STAGE DETECTION SCHEME FOR GLAUCOMA

DISEASE USING AUTOMATED CLASSIFICATION METHOD

M.Kirthikavathi 1*

,M.Mahendraselvi 2*

S.Veluchamy3*

1,2PG Scholar of Electronics and Communication Engineering,Anna University Regional Office,Madurai 7,Tamil

Nadu, India.

3Faculty of Electronics and Communication Engineering, Anna University Regional Office, Madurai 7, Tamil

Nadu, India.

ABSTRACT Glaucoma is an eye disease which causes increased intraocular pressure (IOP) within the eye. This increased IOP in

the eye is usually caused by an imbalance production of aqueous humor in the eye which leads to vision loss. Almost 14 million people have glaucoma. There are no prior symptoms of glaucoma and therefore, early detection of the disease is essential for

preventing one of the most common causes of blindness. Glaucoma detection based on digital images of the retina has been performed in the past few years in the clinics but they still lack robust automated assistance. So there is a need for an automated system that detects glaucomatous eyes and classifies the glaucomatous stages based on acquiring fundus images. In contrast to other approaches, certain higher order cumulant features are extracted using radon transform and by using Linear Discriminant Analysis dimensionality of the extracted feature are reduced. Glaucoma stages are classified using the SVM classifier.

Keywords: Glaucoma, Radon transforms, LDA

I. INTRODUCTION

Glaucoma is a complicated disease that causes

increased fluid pressure inside the eye. This increased

fluid pressure (called intraocular pressure IOP) causes

damage to the optic nerve leads to permanent vision loss. Glaucoma is the second leading cause of

blindness. It is estimated that in India about 11.2

million people are suffering from glaucoma over the

age of 40. And it is surveyed that, in 2020

approximately 79.6 million people are diagnosed with

glaucoma. It has been reported that, people in the age

group of between 40 and 50 had elevated IOP of 2%

and people with an age of above 70 had elevated IOP

of about 8% which leads to blindness. Progression of

glaucoma is not detected until the optic nerve gets fully

damaged. So glaucoma screening at regular interval is necessary. It is recommended that persons at the age of

40 - 64 must undergo a regular screening of glaucoma

in every 2to 4 years and people at the age of above 65

are must undergo glaucoma screening for once in

every 2 years. Glaucoma detection based on manual

diagnosis system such as tonometry, ophthalmoscopy,

perimetry, gonioscopy, and Pachymetry may require

more manpower and more time. Medical management,

drainage implants, laser surgery and trabeculectomy.

Mass screening of patients helps for early stage

detection of glaucoma and helps to prevent patients

from surgery. A central reason to hope for automated glaucoma diagnosis is that it could be used as a tool for

mass screening and also minimizing the required

manpower and time required to test large number of

people.

A. RELATED RESEARCH WORK

Siamak Yousefi et al. Proposed method for

glaucoma using Optic nerve (ONH) features. From his

method Optic nerve head’s (ONH) geometric

parameters are also used to measure the progression of glaucoma. The geometric parameters of optic nerve

head measure any changes in the structure of ONH such

as the area of the optic disk (OD), the diameter of the

optic disk, area of the rim, cup diameter and mean cup

depth. High IOP, astrocytes and optic nerve fiber

deterioration are the main characteristics of glaucoma.

Optic nerve fiber deterioration causes decrease in

thickness of the retinal nerve fiber layer (RNLF).

Astrocytes and axon degeneration changes the ONH

configuration and this leads to the decrease in functional

capability of the retina. Stereo fundus images are mostly

used by ophthalmologists to measure the rim, disk diameter [1].

Sushma G.Thorat et al. uses digital fundus images

and proposed that digital fundus image analysis is very

useful in understanding the natural development of

disease which is based on computational technique to

make a qualitative assessment of the eye. These

methods reduce the inter and intra observer variability

errors which are arising during the screening of disease

by the doctors [2].

Sandra Morales et al. proposed glaucoma detection

method based on morphological features. Different morphological features such as cup, disk, disk diameter,

rim area and cup to disk ratio (C/D) obtained from a

fundus image can help to treat glaucoma [3]. C/D ratio

is one of the key parameters used by ophthalmologists

while diagnosing glaucoma. A normal OD contains

* [email protected]


46

greater than 1.2 million fibers passing through it which

makes the cup size very small but glaucoma causes

ONH to lose the RNLF resulting in an increase in the

size of the cup. Progression of glaucoma leads to

enlarging the size of the cup this in turn leads to

increase in cup-to-disk ratio [6]. Depending upon the C/D ratio glaucoma can be

classified into three stages, namely Mild glaucoma,

Moderate glaucoma and severe glaucoma. At mild

glaucoma stage the C/D ratio is normally 0.4. Moderate

glaucoma have C/D ratio of about 0.7 and severe

glaucoma have C/D ratio of more than 0.7[4].Since

visual fields [1] and IOP measurement are not suitable

for mass screening, OD detection plays a major role in

diagnosing glaucoma. Geometric parameter

model[1],super pixel algorithm[2],mathematical

morphological method[3],wavelet based method [5]

fuzzy convergence[11],the contour model based approach[9]template matching[12],deformable models

and hough transform [8] are the few techniques used

automatic detection of ONH. Haralick texture [4],

fractal [8] and higher order cumulant [HOS] [7] features

are also used for glaucoma diagnosis.

This paper is organized as follows. The proposed

system is explained in section II, including the

preprocessing steps, feature extraction by radon

transform and dimensionality reduction by LDA.

Section III describes the results and discussion about

proposed work and section IV and V deals with conclusion and feature work respectively.

(a) (b)

(c) (

d)

Fig. 1.Fundus images: (a) normal, (b) mild glaucoma, (c) moderate

glaucoma (d) severe glaucoma

II. PROPOSED WORK

A. DATA COLLECTION

Human fundus images are the database used here.

Fundus images are obtained from the digital fundus

camera and are taken by the well trained

ophthalmologists. Fundus cameras are used to exam the

retinal disease. A modified digital back unit is

connected to the fundus camera to convert the fundus

image into a digital image. These digital images are processed and stored on the hard drive of a Windows

based computer with a resolution of 768 x 576 in JPEG

format. This consists of 8-bits of RGB layers with 256

levels each. The images are linked to the patient data

using the Visupac software, which is a patient database.

The images are usually obtained from the posterior

pole’s view, including the optic disc and macula. Three

different classes of fundus image are used there in this

project. Firstly normal fundus images without any

glaucoma infection. Secondly the fundus image with

mild glaucoma infection. Thirdly the fundus images

with severe glaucoma infection. Generally the database is obtained from the

department of ophthalmology in any reputed hospital or

the database is easily available on the internet in the

form of DRIVE database. In this paper totally 69

glaucoma (including normal, mild, severe glaucoma)

images are used.

Fig. 2. Block diagram for the proposed system

B. PRE-PROCESSING

Patient movement, bad positioning, poor focus,

inadequate illumination and reflections can cause a

significant proportion of images to be of such poor quality as to interfere with analysis. In the retinal

images there can be variations caused by the factors,

including differences in cameras, acquisition angle,

illumination and retinal pigmentation. Initially the

retinal color fundus image is converted into gray image

and then noise in the images is removed by using

filtering process. During the image capturing process,

photon noise may present due to the intrinsic property

of light. This noise is removed by using median filter.

Comparing to other filters, median filter removes the

noise as well as preserves the edges of the image.

Glaucom

a infected

images

Preprocessi

ng

Feature

Extraction

Feature

Ranking

Classificat

ion

Dimensional

ity

Reduction


47

We use 2D median filter for highlighting and removing

noise from the Morphological open function.

(1)

Here represents a neighborhood

centered on location (m, n) in the image.

C. FEATURE EXTRACTION

Higher order cumulant (HOC) features are extracted

using radon transform. HOC features are extracted for

every 10o.

a. Radon Transform

Radon transforms in 2D is an image projection

operation. It has a special property that it projects the

image intensity along radial line which are oriented in specific angle. This property is very helpful in finding

the features along line integrals. Applying radon

transform for a given set of specific angles in the image

is similar to computing the projection of image in

given angle. The obtained projection is the sum of

intensities of pixels in each direction.

Fig. 3.Radon transform

Let ƒ(x) = ƒ(x,y) be a compactly supported continuous

function on R. The Radon transform (Rƒ), is a function

defined on the space of straight lines L in R by the line

integral along each such line

Concretely, the parameterization of any straight line A

with respect to arc length t can always be written

Where p is the distance of A from the orgin and β is the

angle the normal vector to L makes with the x axis.

It follows that the quantities (β,p) can be considered as

coordinates on the space of all lines in R, and the

Radon transform can be expressed in these coordinates by

(5)

More generally, in the n-dimensional Euclidean

space R, the Radon transform of a supported

continuous function ƒ is a function Rƒ on the space

Σn of all hyperplanes in R. It is defined by

Where the integral is taken with respect to the natural

hypersurface measure, dσ and for 2-dimensional case

generalizes the dy term. Observe that any element of

Σn is characterized as the locus of an equation and p is

equal to product of and y.

Where β∈ pn−1 is a unit vector and p ∈ R. Thus the n-

dimensional radon transform may be rewritten as a

function on pn−1×R. It is also possible to generalize the

Radon transform still further by integrating instead

over k-dimensional affine subspaces of R. The X-ray

transform is The X-ray transform is obtained by

integrating it over straight line and is most widely used

for this construction.

b. Higher order cumulant features

Higher order cumulant features are used in proposed

system and are calculated using radon transform. The

first two order statistics have been used extensively in

bio- signal processing. The first two order moments

and power spectral density (PSD) derived from

moments are the first two order statistics. However,

most of the bio-signals are nonlinear, non-stationary

and non-Gaussian in nature, and thus need to model the

higher order statistics of the signal namely third and

fourth order statistics. The higher order spectra

cumulant are the higher order correlations of the given

signal. By using higher order moments higher order correlations are derived. HOS are used in the analysis

of epileptic EEG signals, automated analysis of sleep

stages, cardiac health diagnosis etc.

Let Y (k), k=0, ±1, ±2, ±3, ±4…is a digital signal to

be sampled. Their first four moments are defined as

(7)

(8)

(9)

http://en.wikipedia.org/wiki/Continuous_function

http://en.wikipedia.org/wiki/Continuous_function

http://en.wikipedia.org/wiki/Line_integral#Definition

http://en.wikipedia.org/wiki/Line_integral#Definition

http://en.wikipedia.org/wiki/Euclidean_space

http://en.wikipedia.org/wiki/Euclidean_space

http://en.wikipedia.org/wiki/Hyperplane

http://en.wikipedia.org/wiki/Measure_(mathematics)

http://en.wikipedia.org/wiki/X-ray_transform

http://en.wikipedia.org/wiki/X-ray_transform


48

(10)

The nth order moment is given by

, , )

=

The first 4 cumulants of a zero mean process are

defined as

(12)

(13)

(14)

(15)

The nth order cumulant can be calculated using nth

order moment as follows

(16)

Where is the nth order moment of an

equivalent Gaussian process and is

the nth order moment function.

Higher order cumulant features are thus extracted

and in order to reduce the dimensionality of obtaining

features, linear discriminant analysis (LDA),

independent component analysis (ICA) and principal

component analysis (PCA) techniques were used. Among three LDA provides better classification

accuracy

D. DIMENSIONALITY REDUCTION

LDA classifies the data by providing highest

possible discrimination between different classes of

data. When the data set is projected to different space

PCA modifies the location and shape of the data, but

LDA preserves it with maximum class of separability.

It maximizes the ratio of between class variance to

within class variance and hence it guarantees

maximum class of separability. Apart from the

dimensionality reduction LDA also provides maximum discriminating of classes.

LDA seeks to reduce dimensionality while

preserving as much of the class discriminatory

information as possible .Assume we have a set of -

dimensional samples (1, (2, … ( , 1 of which

belong to class 1, and 2 to class 2. To obtain a

scalar by projecting the samples onto a line =

.Of all the possible lines we would like to select the one that maximizes the separability of the scalars.

Good projection vector is obtained by defining a

measure of separation.

The mean vector is given by

We would then select the distance between the

projected means as our objective function

Maximum discrimination of classes can be obtained by

(i) Within class scalar matrix

(22)

(ii) Between class scalar matrix

(ii) Mixture scatter matrix

Similarly, we define the mean vector and scatter

matrices for the projected samples as


49

(28)

From our derivation, two-class problem can be written

as

Choose the projection that maximizes the ratio of

between-class to within-class scatter. Since the

projection is not a scalar (it has −1 dimensions) for

longer, we use the determinant of the scatter matrices

to obtain a scalar function.

is the sum of matrices of rank ≤1 and the mean

vectors is constrained

III. RESULTS AND DISCUSSIONS

Initially a fundus image of dimension 256X170 is

given as input. This fundus image is taken from

DRIVE database. Given a color input is converted into a gray image by using RGB to GRAY conversion

method and noise in the images is removed using

median filtering. Then by using radon transform higher

order cumulant features are obtained and the

dimensionality of these features is reduced using LDA.

Better sensitivity can be achieved from fundus

image using higher order cumulant features. Different

morphological features such as the ratio of the distance

between the cup portions of ONH to the diameter of the OD, C/D ratio is also used for glaucoma detection.

By using PCA only 80% of accuracy is obtained for the

SVM classifier. Texture analysis, fractal and power

spectral features, wavelet features and HOS are also

producing a lesser accuracy compared to higher order

cumulant features.

The result obtained from this experiment is as follows.

Fig. 4.RGB to GRAY Conversion

Figure 4 shows the conversion of RGB fundus image to gray color

image.

Fig. 5.Noise removed image

Figure 5 shows the noise removed image. .Fundus images

are having photon noise during capture process and are

removed using median filter.

Fig. 6.Radon transform output

Figure 6 shows the feature extraction using radon

transform. For every 10 degree features are extracted.

Fig. 7 LDA Output

Figure 7 shows the output of LDA technique. If there are

N classes, LDA reduces the dimensionality into N-1 classes. In this paper totally we have three classes and so

we get two sets of LDA features.

IV. CONCLUSION

If glaucoma is not treated in its initial stage it will lead to

permanent vision loss and also glaucoma does not have any

prior symptoms. In order to prevent vision loss earlier

detection of glaucoma is necessary. This paper provides a

new method for early detection of glaucoma using higher

order cumulant features. Higher order cumulant features are extracted using radon transform and the dimensions of the

obtained features are reduced using linear discriminant

analysis.


50

V. FUTURE WORK

As a future work, from the obtained higher order cumulant features some of the most significant set of features are selected by using feature selection technique. Feature selection technique ranks all the features and among them features which are having top rank are used for classification. Feature ranking enhances the accuracy of classification. Classification is done by using Support Vector Machine (SVM) classifier. The main aim of this paper is to find a maximum margin hyperplanes in the feature space which can separate negative and positive examples from each other. Among all classifier types SVM can classify the unknown data because it has higher generalization ability and so for better classification SVM classifier can be used.

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51

FEATURE EXTRACTION USING SIFT FOR MAGNETIC RESONANCE IMAGES

Sathiyaseelan. J1,, Mr. C. John Moses2

1,2 ECE dept. St. Xavier’s Catholic College of Engineering, Nagercoil,

Abstract— since the memory capacity of the images are high, storage of these images and files impose a huge

barrier in medical field. This project involves feature extraction using SIFT algorithm (Scale Invariant Feature

Transform) for MR (Magnetic Resonance) images. Here the quality of an image is increases and noise is reduced.

Feature extraction is the process of transforming input data into set of features. The study also investigates the feature

extraction and effective memory size reduction.

I. INTRODUCTION Magnetic resonance (MR) image is a type of scan

use to know the body defects with the help of signals

from normal and abnormal areas of the body which help

the doctors. To identify the defects this is the advance

technology over CT scan which neglects usage of X-

rays. It is more accurate and precise. Usually it is used

for brain, spine and joint imaging.

Feature extraction is nothing but defining or

identifying various features and extracting them from

the input data or image. Feature extraction is

transforming the input data into the set of features. It

integrates computation, programming is easy to use environment. Feature extraction will reduces the

resources need for desiring a data. MATLAB is a

language where the problems and the solutions were

represented in mathematical notation.

A robust set of methodologies to reduce the

design power consumption, X power for designed based

power analysis, web based power tools. The system

generator basics provides two key tools one is blocks for

building model and other hardware generator is model

into HDL code generator test vector with MATLAB and

Simulink block sets using HDL coder, generate VHDL

and Verilog code for Xilinx FPGA’s from MATLAB, Simulink and state flow model. Advantage of system

generator is to reduce the loss of data precision and save

time by skipping a step during code generation.

II. MATERIALS AND METHOD

In fig-1, the input source MR image should

have some other noise so that the noise has to be

smoothened by the Gaussian filter and the filtered

output will send to difference of Gaussian it will

identify the difference between two images. To increase the contrast of an image histogram equalization will be

used. The key point detection s used to discard low

contrast key points and then filters out those located on

edges. Each key points assigned one or more orientation

based on local image gradient direction the magnitude

and direction calculation for the gradient are done for

each and every pixel in a neighboring region. Then to

compute a descriptor vector for each key point such that

the descriptor is highly descriptive and partially

invariant to the remaining variations.

The Simulink blocks for MATLAB, where the

blocks are taken from various blocks from Simulink and

the Xilinx block for the gateway in, gateway out and system generator were used. The vertex 6 and other

logical devices were used to compute the design report,

RTL schematic, timing report and power analysis report.

This should calculate for various MR images.

Fig-1 Block diagram of SIFT for feature extraction

Alogrithm:

Step 1: Read the input Magnetic Resonance image of

brain.

Step 2: Convert RGB image into gray scale image

Step 3: Gaussian filtered is applied to an image.

Step 4: Histogram equalization is applied to increasing the contrast of an image.

Step 5: Edge detection is performed by key point

detection.

Step 6: Gradient magnitude and orientation is calculated

for image direction

Step 7: Finding key points and processing time for

various images.

A. Gaussian Filter

52

Gaussian filter is use to get original structure of the

input data by filtering the noise from the input data or

image. Gaussian filter is use to eliminate the noise and

to smooth the image. First convolution between source

image and Gaussian function, the result is a Gaussian

blurred image.

yxIyxGyxL ,*,,,,

Where, Gaussian function

2222/1,, yxezyxG

= Scaling factor

x, y =spatial coordinates

B. Difference of Gaussian

Difference of Gaussians (DoG) is an algorithm

which is used for finding the difference between two

filtered images. The subtraction between blurred image

and an original image from input, less blurred version of the original image. In the simple case of grayscale

images, the blurred images are obtained by convolving

the original grayscale images with Gaussian kernels

having differing standard deviations. The range of

frequency that preserves the blurred images in spatial

information.

,,,,,, yxLkyxLyxD

Where,

k – Kernel factor

C. Key point Detection

Localization of key points using DoG is carried

out here both high contrast and low contrast point are

detected, only the high contrast point are consider

whereas the low contrast point are neglected with the help of these high contrast point location, scale, rotation

have been done in order to find the key points in

accurate manner. After scale space extrema are detected

the SIFT algorithm and then filters out those located on

edges. Resulting set of key points is shown on output

image. The detection taken here by Scale-space extrema

produce many key points some of these are unstable.

D. Gradient Magnitude And Orientation

It is the necessary to identify the image even through the

scale, rotation and orientation are different for this

purpose the gradient magnitude and orientation is done with the help of invariance key points. Each key point is

assigned one or more orientations based on local image

gradient directions.

The Gaussian-smoothed image L at

the key point's scale σ is taken so that all computations

are performed in a scale-invariant manner. For an image

sample at scale σ, the gradient

magnitude, , and orientation, , are

precomputed using pixel differences:

Where,

Vertical Direction yxLyxLGx ,1,1

Horizontal Direction 1,1, yxLyxLGy

221,1,,1,1, yxLyxLyxLyxLyxm

yxLyxLyxLyxLsayx ,1,1,1,1,tan,

The magnitude was calculated for the gradient

were done for each pixel in the region of key points of

Gaussian blurred input image. The orientation histogram

of 36 bins is formed, each has 10 degrees. Each sample

in the neighboring window added to a histogram bin is

weighted by its gradient magnitude and by a Gaussian-

weighted circular window with a scale factor that is 1.5

times that of the scale of the key point.

E. Feature Descriptor

Key point locations of an image at particular

scales and assigned orientations. These ensures

invariance to image location, scale and rotation. Now

we want to compute a descriptor vector for each key

point such that the descriptor is highly distinctive and

partially invariant to the remaining variations such as

illumination, 3D viewpoint, etc.

The set of orientation histograms is created on

4x4 pixel neighborhoods with 8 bins each. These

histograms were computed from magnitude and

orientation values of samples in a 16 x 16 region around

the key point such that each histogram contains samples from a 4 x 4 sub region of the original neighborhood

region. The magnitudes are further weighted by a

Gaussian function with equal to one half the width of

the descriptor window. The descriptor then becomes a

vector of all the values of these histograms. Since there

are 4 x 4 = 16 histograms each with 8 bins the vector

has 128 elements. This vector is then normalized to unit

length in order to enhance invariance to affine changes

in illumination. To reduce the effects of non-linear

illumination a threshold of 0.2 is applied and the vector

is again normalized. Although the dimension of the descriptor,

seems high, descriptors with lower dimension than this

don't perform as well across the range of matching tasks

and the computational cost remains low due to the

53

approximate BBF method used for finding the nearest-

neighbor. It is also shown that feature matching

accuracy is above 50% for viewpoint changes of up to

50 degrees. Therefore SIFT descriptors are invariant to

minor affine changes. To test the distinctiveness of the

SIFT descriptors, matching accuracy is also measured against varying number of key points in the testing

database, and it is shown that matching accuracy

decreases only very slightly for very large database

sizes, thus indicating that SIFT features are highly

distinctive.

III RESULT

In fig-2 the input image is the brain images from

the image processing tool box. This image is filtered

using Gaussian filter and the noise will be removed.

Histogram is used for increases the contrast and enhance an images for the filtered image will increases

the intensity of an image by histogram equalization

process. The Canny edge detector is an edge

detection operator that uses a multi-stage algorithm to

detect a wide range of edges in images. The pixel values

should change edges length of an image. And then

enlarge size of an image.

Fig-2 Input brain MR image

Input MR

Images

Knee

(sv)

Knee

(lv)

Joint

(sv)

Joint

(fv)

Shoulder

(fv)

Brain

(cssv)

Skull

(cssv)

Spinal

cord

(cssv)

Brain

(cstv)

REAL time to

Xst completion

(seconds)

14 s 16 s 11 s

12 s 15 s 15 s 10 s 11 s 9 s

Memory size

(Kbits)

210900 209236 210900 208724 210644 208852 208532 210900 210900

CPU time to Xst

completion

(seconds)

13.70 s 16.24 s 10.80 s 12.45 s 15.02 s 15.20 s 10.39 s 11.22 s 9.47 s

Table- Result for computation time Various MR imageIn

this table the various input MR images are shown for

feature extraction. The real time computation CPU time

computation and memory size were calculated in these table

sing Simulink.

Fig-3 Output of key points extraction

The key point detection should carried out by

varying the scaling factor. The key point detection output.

The key points are extracted and shown in the workspace of

MATLAB the time to finding the key points and total no of

key points extracted

Fig-4 Key point extraction of an image

54

IV. DISCUSSION

In the system we have the software part which

reduces the memory size of an image and increases the

processing speed of an image. The memory size, real time and

CPU time will be computed in the table for various images.

Fast SIFT method can also be used for further improvement.

V. CONCLUSION

The SIFT algorithm has been a known extracting

features from a input image. Hardware acceleration is often

needed in many computer vision applications. In this project

the SIFT algorithm is faster for feature extraction and

processing speed is less. This design enables a streaming type

of data flow that uses a 50% less memory and calculate

processing time for various MR images. This project will be

develop by increasing the processing speed of the MR image.

VI. REFERENCES

[1] Chao-Yung Hsu, (2010)” Secure And Robust Sift with Resistance

to Chosen-Plaintext Attack” Proceedings of 2010 IEEE 17th

International Conference on Image Processing September26-29 pp

997-1000.

[2] Chao-Yung Hsu (2012) “Image Feature Extraction in Encrypted

Domain With Privacy-Preserving SIFT” IEEE transactions on

image processing, vol. 21, no. 11 pp 4593-4607.

[3] Claudia Chevrefils,(2009)” Texture Analysis for Automatic

Segmentation of Intervertebral Disks of Scoliotic Spines From MR

Images” IEEE transactions on information technology in

biomedicine, vol. 13, no. 4 pp 608-620.

[4] David G Lowe,(2004)”Distinctive Image Features from Scale-

Invariant Key points” International Journal of Computer Vision

pp 1-28.

[5] Feng-Cheng Huang,(2012) “High-Performance SIFT Hardware

Accelerator for Real-Time Image Feature Extraction” IEEE

transactions on circuits and systems for video technology, vol. 22,

no. 3 pp 340-351.

[6] Herbert Bay1, Tinne Tuytelaars(1998)” SURF: Speeded Up

Robust Features”IJCV 30(2) pp 1-14.

[7] Hong Liu, Hong Lu,(2010)”SVD-SIFT for Web near Duplicate

Image Detection” IEEE transactions on information technology

ICIP , vol. 13, no. 4 pp 1445-1448.

[8] Jayasimha Rao, Noora Partamies,(2014) ”Automatic Auroral

Detection in color Allsky Camera Image” IEEE transactions on

circuits and systems for video technology, vol. 24, no. 7 pp 1474-

1481.

[9] Jie Jiang, Xiaoyang Li,(2014)” SIFT Hardware Implementation for

Real-Time Image Feature Extraction” IEEE transactions on

circuits and systems for video technology, vol. 24, no. 7 pp 1209-

1220.

[10] Kazu Mishiba,(2013)”Image Resizing with SIFT Feature

Preservation” IEEE transactions on image processing ICIP, vol.

12, no. 10 pp 991-995.

[11] Kosuke mzuno et al. (2011) “A low power Real-time SIFT

Descriptor Generation Engine for Full-HDTV Video

Recognition”IEICE trans electron vol e94-c no 4 pp 448-457.

[12] Liang-Chi Chiu (2013)“Fast SIFT Design for Real-Time Visual

Feature Extraction” IEEE transactions on image processing vol.

22, no. 8 pp 3158-3167.

[13] M V S Lakshmi, Ravi Mathey, (2013)”Encrypted Feature

Extraction for Privancy SIFT” IEEE 17th International Conference

on Image Processing ISSN vol 1, issue 12 pp 23-26.

[14] Nithya Priya, Sasikumar,(2014)” Detection and Segmentation of

Brain Tumor using Adaboost SVM” IJIRCCE ISSN, vol. 2, special

issue1 pp 2323-2328.

[15] Peter I. Rockett,(2003)” Performance Assessment of Feature

Detection Algorithms: A Methodology and Case Study on Corner

Detectors” IEEE transactions on image processing, vol. 12, no. 12

pp 40-47.

[16] Rukun hu et al.(2009) “Investigating Visual Feature Extraction

Methods for Image Annotation” IEEE international conference on

system pp 3122-3127.

[17] Salim Lahmiri and Mounir Boukadoum,(2011)” Classification of

Brain MRI using the LH and HL Wavelet Transform Sub-bands”

IEEE 978-1-4244-9474-3/11 pp 1025-1028.

[18] Shuai Lu,Qinging Zhao,(2013)”MultiScale, Multi Level,

Heterogoneous Features Extraction and Classification of

Volumetirc Medical Images” IEEE transactions on information

technology in biomedicine, vol. 13, no. 4 pp 1418-1422.

[19] Wei Tu Chen, Wei Chuan Liu,(2010)”Adaptive Color Feature

Extraction Based on Image Color Distribution” IEEE transactions

on image processing, vol. 19, no. 8 pp 2005-2016.

[20] Xudong L U,(2012)” Perceptual Image Hashing Based on Shape

Contexts and Local Features Points” IEEE transactions on

information forensics and security, vol. 7, no. 3 pp 1081-1093.

[21] Yahao Zhau, Qiue Yu,(2013) “An Automatic Global to Local

Image Registration Based on SIFT and Thin Plate Spline” IEEE

transactions on image processing, vol. 21, no. 11 pp 2535-2538.

[22] Yao Shen, Parthasarathy Guturu, (2009)” Video Stabilization

Using Principal Component Analysis and Scale Invariant Feature

Transform in Particle Filter Framework” IEEE transactions on

consumer electronics, vol. 55, no. 3 pp 4343-4356.

[23] Ye zhang and Qingmao hu,(2008) “A PCA Based Approach to the

Representation and Recognition of MR Brain Midsagittal Plane

Images”IEEE EMBS conference pp 3916-3919.

[24] Atiq Islam, Syed M. S. Reza, (2013)” Multifractal Texture

Estimation for Detection and Segmentation of Brain Tumors”

IEEE transactions on biomedical engineering, vol. 60, no. 11 pp

3204-3215.


55

Simulation of Critical Home Health Monitoring & Drug Delivery

System

N. Abhinaya, G. Hari Krishnan, R. J. Hemalatha, G. Umashankar Department of Biomedical Engineering,

Sathyabama University, Chennai

ABSTRACT

The interface of enabling technology with advanced product design has shown radical development in the field of intelligent sensor embedded system design. Numerous applications are envisaged exploiting this inter-connectivity, particularly, in the field of biomedical applications. A need, for example, that is of growing demand,

is in the field of remote health monitoring and control of critically ill patients, with the help of networked sensors. The continuous monitoring of the health of a patient in a hospital, information fusion from multiple sensor data as well as broadcasting the recorded data on a network for the ease of access to the clinician and implementing the decisions of clinicians through automated drug delivery units could save millions of precious lives in a country with limited medical experts. In what follows is a brief detailed description of such a system that is developed. `Keywords: Home health care, Drug delivery system, embedded system, LabView, Patient, Healthcare.

INTRODUCTION

The monitoring of various parameters of a pa-

tient in critical care, like blood pressure, temper-

ature, heart rate is extremely important, to have a

continuous evaluation of the critically ill patients

[1]. The real time information of these parame-

ters is quite useful for medical services and if it

is easily accessible from a remote area for a cli-

nician then it can solve a major problem of phys-

ical absence of medical experts, in many places

of the developing World [3 & 4]. The measured

parameters are interfaced with LabView installed in a host PC using the Data acquisition unit of

National Instruments. The interfaced data are

further processed by using a suitable information

fusion algorithm. The net information is broad-

casted to client PC which is connected to the host

PC via use of web protocols of LabView [2&5].

This help in monitoring the parameters through

PC’s connected via intranet or internet with each

other. Finally drug delivery control system which

will have a data acquisition controlling and

broadcasting unit, display unit, and drug control-ling unit.

As the rapid development of information tech-

nology and automation technology, which in-

creases efficiency of industry greatly, is making

the patients of hospital wards and the health care

workers in the automatic association. Each bed

of the hospital wards and nurse room need long-

distance automatic call from time to time, which

is easy for patients in urgent of medical signal,

and the signal staff can also deal with relevant

affairs quickly and efficiently [7].

The proposed system approaches designing a

home health monitoring and drug control system

which can be considered as an integrated part of

three subsystems multi‐sensor data acquisition,

information fusion networking and data broad-

casting and automated system control [6]. This

multiple channel intelligent system would meas-

ure the parameters like blood pressure, tempera-

ture, heart rate etc Next, the measured parame-ters are interfaced with LabView installed in a

host PC using the Data acquisition unit of Na-

tional Instruments. The interfaced data are fur-

ther processed by using a suitable information

fusion algorithm. The net information is broad-

casted to client PC which is connected to the host

PC via use of web protocols of LabView. This

help in monitoring the parameters through PC’s

connected via intranet or internet with each other

[8].


56

For a patient who is in coma state, or who be-

came disabled due to some accidents or some

other cases need to be treated for a long time. It’s

difficult for people to afford the expense in the

hospital for such a long period. This project will

be more helpful for such people, where the pa-

tients can be monitored from the home itself.

Daily doctor can check the condition of the pa-tient without the physical presence and even if

some abnormalities are found the drug can be

given to the patient. The drug and the dosage of

the drug are controlled by the doctor through the

doctors control panel [9].

MATERIALS & METHODS

The project is done using LabView. LabVew

(short for Laboratory Virtual Instrumentation

Engineering Workbench) is a platform and de-

velopment environment for a visual program-

ming language from National Instruments [6].

The graphical language is named "G" originally

released for the Apple Macintosh in 1986.

LabView commonly used for data acquisition,

instrument control, and industrial automation on a variety of platforms including Microsoft Win-

dows, various flavors of UNIX, Linux, and Mac

OS X.

Algorithm

• Read the values from the files as indicat-

ed in figure 1.

• Update the current output status

• Indicate the abnormal values with re-

quired medicine

• Require doctor control

• Necessary actions take over by the physi-

cian

.

Figure 1 Diagramatic representation of algorithm

The main block diagram for reading and control-

ling parameter is as shown in figure 2. The pro-

ject of Home health monitoring system simula-tion results are obtained in LabVIEW develop-

ment system. Process result consists of one nor-

mal condition and six abnormal conditions. In

the file path control normal or abnormal files are

chosen to obtain the results.

Figure 2 Block diagram for reading the parame-

ters

The observed parameters are interfaced with LabVIEW installed in a host PC. The read

data are further processed by LabVIEW algo-

rithm and it will displayed by respective regions.

Doctor control unit is used to apply the values to

drug delivery unit. This also has been imple-

mented in simulation. Front panel consist of

blood pressure, temperature, heart rate and drug

level displays.

RESULT & DISCUSSION

The first condition is taken as normal, if the pa-

tient is in normal condition then no LED’s

glows. The normal temperature, pressure and

heart rates are displayed. In the status display it

won't display a need of medicine instead it dis-

plays the condition is normal. The next condition

is abnormal condition, it can be due to variation in any parameters, and it might be due to abnor-

mal temperature, pressure or heart rate. If there is

some variation in any parameters which are

measured, from the normal values then on the

corresponding front panel the LED glows indi-

cating an abnormal condition. If the patient’s


57

temperature is high then LED glows at tempera-

ture panel and status appears as abnormality in

temperature and also the medicine which is

needed is displayed in the status.

Likewise if there is any abnormality in other

parameters the same steps are followed. For

knowing the level of the drug which is stored

another indicator is present. If the drug level is

too low, then LED glows with an alert to refill

the drug. It also consists of a doctors control

panel which is the most important part where the

doctor from a remote area can control the drug

delivery to the patient. This consist of mainly

two parts one for selecting the drug and the other

for selecting the dosage.

Figure 3 Front panel display of body temperature

In this front panel the body temperature of the

patient will be displayed both in Celsius and

Fahrenheit as shown in figure 3. The temperature

will be also indicated as in a thermometer level

model. If the patient’s temperature is normal

then the status will be indicating normal. If the

patient is having a high temperature than the

normal level then LED glows like alarm. And

also the drug required and the abnormal condi-tion will be displayed. The normal body tem-

perature is 37C and 98.6F.

In this front panel the heart rate of the patient is

displayed as shown in figure 4. There are two

conditions for abnormal heart rate which are

tachycardia and Bradycardia. Normal heart rate

of an adult is 60-80 beats per minute. Tachycar-

dia is the increased heart rate and bradycardia is

the decreased heart rate. LED glows if there is

increased or decreased heart rate from the normal

condition. In the status the drug required an the

condition of the heart is indicate. In this front

panel blood pressure of the patient is displayed

as shown in figure 5. There are two abnormal

conditions high blood pressure and low blood

pressure. Normal blood pressure of an adult is 80/120 mmHg. If the pressure is more than

90/140 mmHg this indicates high blood pressure. If the pressure is lower than 60/90 mmHg this

indicates low blood pressure. If the patient is

having any pressure abnormalities the LED

glows. Status displays the condition whether it is

high blood pressure or low blood pressure and

also shows the medicine required.

Figure 4 Front panel display of heart rate

Figure 5 Front panel display of pressure


58

In this front panel drug level which is to be given

to the patient is displayed as shown in figure 6. A

tank indicates the level of the drug. When the

drug amount stored becomes low then an LED

glows this is an alarm for refilling the tank.

Figure 6 Front panel display of drug level

Figure 7 Front panel display of doctors control

panel

In figure 7 doctors control panel has been ex-

plained. This consists of tools for selecting the

required medicine and for selecting the dosage of

the medicine. The status of the medicine which is

used will be displayed. The level of the medicine

is also indicated which is all controlled by the

doctor.

SUMMARY & CONCLUSION

In this paper a home health monitoring with drug

delivery under the control of a doctor has been

introduced. Simulation of home health monitor-

ing & feed back control with drug delivery was

done using Lab VIEW. This project helps to monitor the patient from the home itself and doc-

tor can control the drug delivery even from a

remote area. This helps many people to get treat-

ed from their home itself without much expense. The real time implementation of this project can

bring drastic changes, and can save millions of

life.

REFERENCES

[1] Rifat Shahriyar, Faizul Bari MD., Kundu Gourab,

Sheikh Iqbal Ahamed and Mostofa Akbar MD., Sep-

tember 2009 ,“Intelligent Mobile Health Monitoring

System (IMHMS)”, International Journal of Control

and Automation, Vol.2,No.3.

[2] Bratislava, S.K Miroslav, Lehocki Fedor and Valky

Gabriel, 2-7 Jan 2012, “Multi-Platform Telemedicine

System for Patient Health Monitoring”, Proceedings of

the IEEEEMBS International Conference on Biomedi-

cal and Health Informatics (BHI 2012), Hong Kong and

Shenzhen, China.

[3] T. E. Doyle, M. Kalsi, B. Aiyush, J. Yousuf, and O.

Waseem,( 2009), “Non-Invasive Health Monitoring

System (NIHMS)”, Science and Technology for Hu-

manity (TICSTH), IEEE Toronto International Confer-

ence.

[4] Nitin P. Jain, Preeti N. Jain and Trupti P. Agarkar,

2012,“An Embedded, GSM based, Multiparameter,

Realtime Patient Monitoring System and Control -

AnImplementation for ICU Patients”, Information and

Communication Technologies (WICT).

[5] Shubhangi M. Verulkar, Maruti Limkar, June 2012

“Real Time Health Monitoring Using GPRS Technolo-

gy”,International Journal of Computer Science and

Network (IJCSN) Volume 1, Issue 3,

[6] S. Deepika, V.Saravanan, May-2013 “An Implementa-

tion of Embedded Multi Parameter Monitoring System

for Biomedical Engineering”, International Journal of

Scientific & Engineering Research, Volume 4, Issue 5.

[7] Chris A. Otto, Emil Jovanov, and Aleksandar

Milenkovic, 2006, “WBAN-based System for Health

Monitoring at Home”, Journal of Mobile Multimedia,

vol. 1, pp. 307-326.

[8] Lodaya P.N,Wadkar S.P, “Movable patient health mon-

itoring using GPS”Advances in Recent Technologies in

Communication and Computing (ARTCom 2011), 3rd

International Conference on 14-15 Nov. 2011

[9] Alexandros Pantelopoulos and Nikolaos G. Bourbaki,

January 2010, “A Survey on Wearable Sensor-Based

Systems for Health Monitoring and Prognosis”, IEEE

Transactions onSystems, Man, and Cybernetics—Part

C: Applications and Reviews, vol. 40, no.1.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6189089




59

DESIGN AND DEVELOPMENT OF DIAGNOSTIC TOOL FOR STRAIN

INJURIES OF FINGER JOINTS USING FLEX SENSOR

Balakumaran.V1, Bhuvaneshwari.S

2, Nithyaa.A.N

3, Premkumar.R

4

1,2,3,4Department of Biomedical Engineering, Rajalakshmi Engineering College,Chennai-602105, India

Abstract

Repetitive strain injuries (RSIs) are injuries to the musculoskeletal and nervous systems that may be caused by repetitive

tasks, forceful exertions, vibrations or mechanical compression. In recent days majority of the population uses computers

and it is an inevitable part of day to day life. People working as programmers tend to work for long hours. This work culture

strains the joints in the fingers beyond their normal capacity leading irreversible joint damage. If a person loses his fingers

functionality it is a great impairment for him and also to people around. To prevent a person from losing his fingers

functionality an early detection of the condition is important. This research focuses on designing a glove with flex sensor and

a microcontroller to diagnose and measure the extent of joint deformities in hand.

Keyword- flex sensor, microcontroller, glove, Graphical User Interface

INTRODUCTION

REPETITIVE STRAIN INJURY (RSI)

Repetitive strain injuries also known as

cumulative trauma disorders refers to the injuries of

musculoskeletal and nervous systems that may be

caused by strain forceful exertions, vibrations,

awkward position and mechanical compression.

Some of the measures used to asses RSI's are grip

and strength and diagnostic tests such as

Finkelstein's test. General exercise is recommended

for reducing the risk of developing RSI.

Different Causes for repetitive Strain Injury

• Tendinitis

• Tendinosis

• Carpal Tunnel Syndrome(CTS)

Tendinitis is the inflammation of a tendon that

involves larger-scale acute injuries which occurs

mostly in limbs. The severity of Tendinitis vary

according to different individual like rock climbers

develop in fingers , swimmers in their shoulders.

Symptoms are tenderness near the joint, mild

swelling and muscle stiffness.

Tendinosis also known as chronic tendinitis is the

damage of tendon at cellular level. It can be

detected visually or by touch for simple swelling.

The increased water content is detected by

ultrasonography. Symptoms can vary from pain

and stiffness of the tendon, or burning around the

inflamed tendon. Physical therapy and rest is a

common experienced therapy. Surgery is done for

extreme cases.

Carpel tunnel syndrome is a median entrapment

neuropathy that causes pain and symptoms in the

distribution of the median nerve due to its

compression at the wrist in the carpal tunnel. It

may be caused by a combination of genetic and

environmental factors. The main symptoms of CTS

is intermittent numbness of the fingers. Its

treatments include use of corticosteroid injection

and night splints.

MATERIALS AND METHOD

The first step is designing a smart glove which is

mounted with 4.5 inch flex sensors on each finger.

This sensor has a characteristic property of

showing a change in the resistance when it is bent.

By exploiting the change in resistance in the sensor

due to the bend, a smart glove has to be designed

and fabricated for diagnostic purpose.

Flex sensor, a input device whose input is a

constant 5V supply and the corresponding voltage

from the sensor is the output. The output voltage is

affected by the variations in resistance these

variations are to be measured. During zero bend

state, in order to control the voltage from the


60

sensor, the output is passed through a voltage

divider circuit. Then its output is fed to the ADC of

the microcontroller to get the corresponding digital

value for the voltage. ADC used is the internal 10

bit present in the microcontroller. The

microcontroller Atmega 328, sends the data to the PC and it is represented as a bar graph in a GUI.

The data is transferred as individual byte from each

ADC channel in real time. The GUI is designed to

read the values that the microcontroller transmits

through its Rx and Tx lines UART to the

FTDI232RL that sends these values continuously

to the PC on a COM serial port. The values on the

COM are read by the GUI and it is interpreted in

bar graph representation.

Voltage divider is a linear circuit that produces an

output voltage (Vout) that is a fraction of its input

voltage (Vin). Voltage division refers to the partitioning of a voltage among the components of

the divider.

Impedance Buffer provides electrical impedance

transformation from one circuit to another Voltage

buffer used to transfer voltage to the

microcontroller to make reading from the ADC and

also transfer a voltage from a first circuit, having a

high output impedance level, to a second circuit

with a low input impedance level.

Microcontroller- ATmega328 is a single chip

micro-controller created by Atmel which operates between 1.8-5.5 volts and belongs to the megaAVR

series. The high-performance Atmel 8-bit AVR

RISC-based microcontroller combines 32 KB ISP

flash memory with read-while-write capabilities, 2

KB SRAM ,1 KB EPROM, 23 general purpose I/O

lines and 32 general purpose working registers.

Graphical User Interface is designed using

processing whose program can be written in the

text editor as sketches. It reads the real time values

in the ADC that the controller reads and it sends to

the PC and interpreted as a real time bar graph plot

for better diagnosis.

Fig 2. Typical GUI of the system

Fig 4. Working of the system

RESULT AND DISCUSSIONS

The glove was worn by the subject and they were

asked to bend their individual fingers one by one in

increments of 10º till the point where they could

bend the maximum. The corresponding voltage

output from the sensor is recorded for each angle.

Angles of bend are

10,20,30,40,50,60,70,80,90,100,110 till the

maximum the person can bend. There is variability

exhibited by people with fatter hands not able to

bend to the extent that a person with thin fingers

can. It is not an abnormality and it is just because

of the more amount of flesh in their fingers that this

variation arises. The data is tabulated and the graph

is plotted for discussions. This type of study was

carried out for different age and sex. The

recordings here were interesting and have

significant importance.

Subject 1

Age: 21

Ang

le

Ind

ex

Mid

dle

Rin

g

Litt

le

Thu

mb

0 4 4 4 4 4

10 3.9

87

3.94

4

3.9

01

3.9

22

3.92

2

20 3.9

44

3.92

2

3.8

58

3.8

58

3.81

5

30 3.8

79

3.90

1

3.7

85

3.8

15

3.75

40 3.8

75

3.87

9

3.7

72

3.7

93

3.70

7

50 3.7

5

3.83

6

3.7

07

3.7

07

3.66

4

60 3.7

07

3.77

2

3.5

34

3.6

85

3.66

4


61

70 3.6

64

3.71

7

3.4

7

3.6

25

3.54

2

80 3.6

21

3.47

8

3.2

97

3.5

34

3.49

9

90 3.5

13

3.38

4

3.1

9

3.4

91

3.49

1

100 3.4

05

3.31

9

3.1

68

3.3

19

Table 5.1 Subject 1 data recorded

Fig 5.1 Graph for Subject 1

From the study it is clear that there is a significant

increase in the resistance offered by the sensor with

the bending of the fingers. Here it is clear that

when a person suffers an abnormality and is not

able to bend the finger beyond a point and not able

to clinch fingers, there is a significant low

resistance offered by the sensor than compared to

condition were the person is able to close fingers

fully. This results in a smaller voltage drop in case

of abnormality and this is the data provided for

diagnosis and the person can be suggested with

treatment. This method is purely a screening test

and after detecting the abnormality the doctors will

have to find the exact nature of the problem and

provide treatment to the patients.

Fig 7. Output of the system

CONCLUSION AND FUTURESCOPE

Many people nowadays suffer from RSI even

without knowing they are having a problem with

their fingers. Most neglect not being able to bend

fingers as a problem. A majority of population

work in IT field and are suffering various

complications due to their work nature. A

diagnostic method is not available to diagnose RSI

of the fingers. Thus our system will fill the gap in

providing an easy and reliable system for diagnosis

of RSI. The resistance change are represented in a

GUI, which provides a means for easy diagnose in

real time. The glove is user friendly and can be

worn and removed with ease. It does not affect any

natural degrees of motion of the fingers. It can be

worn during a subject is carrying out a work and

bending can be recorded. This system provided real

time data thus making the process easy and

removes delays in interpreting the data and result

generation.

The system uses wires to connect to the computer

and the glove this can be replaced with a wireless

connectivity to improve convenience and user

friendliness. It can be used to record data to a vast

population and the data process using database and

we can use pattern recognition system to generate

an automated diagnosis in computer without a

doctor to interpret the data.

REFERENCES [1] Ali, A.M.M., Parit Raja, Ambar, R. ; Jamil, M.M.A. ; Wahi,

A.J.M. ; Salim; Artificial hand gripper controller via Smart

Glove for rehabilitation process, Biomedical Engineering

(ICoBE), 2012 International Conference, 27-28 Feb. 2012,pp

300 - 304

[2] Axisa, F.Gehin, C. ; Delhomme, G. ; Collet, C. ; Robin, O. ;

Dittmar, A.; Wrist ambulatory monitoring system and smart

glove for real time emotional, sensorial and physiological

analysis Engineering in Medicine and Biology Society, 2004.

IEMBS '04. 26th Annual International Conference of the IEEE

(Volume:1), 1-5 Sept. 2004,pp:2161 - 2164

[3] FTDI 232 R datasheet

[4] Getting Started with Processing by Casey Reas and Ben Fry.

[5] Laura Dipietro, Angelo M. Sabatini, Paolo Dario; A Survey

of Glove-Based Systems and Their Applications IEEE

transactions on systems, man, and cybernetics—part c:

applications and reviews, Vol. 38, no. 4, July 2008

[6] Learning Processing: A Beginner's Guide to Programming

Images, Animation, and Interaction by Daniel Shiffman.

[7] Lisa K Simone and Derek G Kampe; Design considerations

for a wearable monitor to measure finger posture, Journal of

NeuroEngineering and Rehabilitation March 2005,2:5

doi:10.1186/1743-0003-2-5

[8]http://www.atmel.com/images/atmel-8271-8-bit-avr-

microcontroller- atmega48a-48pa-88a-88pa-168a-168pa-328-

328p_datasheet.pdf

[9] http://www.bionicgloves.com/default.asp

[10] http://www.cyberglovesystems.com/products/cyberglove-

ii/overview 52

[11] http://en.wikipedia.org/wiki/Power_Glove

[12] http://www.ti.com/lit/ds/symlink/lm124-n.pdf

[13] http://en.wikipedia.org/wiki/Buffer_amplifier

[14] United States Patent, Patent Number: 5,347,843, Date of

Patent: September, 10, 1991


63

CLUSTER ANALYSIS OF GENE EXPRESSION DATA USING

OPTIMIZATION TECHNIQUES: A SURVEY

B. Nithya 1 1, R. Rathipriya 2

2

1,2, Department of Computer Science, Periyar University, Salem -636 011, Tamilnadu, India.

Abstract

The microarray data analysis is one of the most important things for functional genomics research. The development of microarray technology produces massive gene expression data sets. Clustering is the process of grouping genes into set of

disjoint classes called gene clusters so that genes within a cluster are highly similar with one another and dissimilar with the genes in other clusters. Applying traditional clustering techniques such as Hierarchical clustering, k-means, C-means, Self-Organized Maps Clustering (SOM) for this type data is tedious. Therefore, this paper studies the available clustering techniques using optimization techniques like, Particle Swarm Optimization (PSO), ACO, BCO, etc., in the literature and summaries the clustering using optimization techniques for microarray data.

KEYWORD: Gene Expression data, clustering, PSO, K-means, C-means.

Introduction:

Gene Expression [14] is the process by

which information from gene is used in the synthesis

of a functional gene product. These products are

often proteins, but in non protein coding genes such

as rRNA genes or tRNA. Genes, the product is a

functional RNA. Each data point produced by a DNA microarray hybridization experiment represents the

ratio of expression levels of a particular gene under

two different experimental conditions.

Clustering is the unsupervised learning

problem. A clustering is the set of clusters, usually

containing all genes in the microarray datasets. It is

defined as the process of organizing genes into

groups whose members are similar in some way. A

cluster is a collection of genes which is “similar”

between them and are “dissimilar” to the genes

belonging to other clusters. There are mainly two categories of clustering. The first one is hierarchical

method and the other one is partitional method.

Partitional method will divide the genes to various

clusters based on some conditions. Many diverse

clustering techniques have extensively been under

development of the clustering. The most widely used

techniques in analysis of gene expression data which

are applied in the early stages and proven to be useful

are Hierarchical clustering , K-means clustering and

Self-organized maps (SOM).

Microarray data clustering analysis:

Clustering gene expression data can be divided into the three groups,

1) gene-based,

2) sample-based and

3) Subspace clustering as both

genes and samples is required to clustered

genes.

Gene-based clustering: The gene-based clustering is divided into

group of genes together in co- expressed genes which

indicate co-function, co-regulation and reveals the

natural datasets of genes. The genes are treated as the

cluster, while the samples of the features. Clustering

algorithms for gene expression data should be

competent of extracting the microarray genes from a

high level of background noise. A clustering

algorithm should depend as possible on prior

knowledge and also gives a graphical representation

of the cluster structure and the other than partitioning

the gene expression datasets.

Sample-based clustering:

To find the substructure of the microarray

data, regards the samples as the genes and the group

of genes as the features. Genes are generally related

to different infection or medicine effects within a

gene expression matrix. Only a minute subset of

genes whose gene expression levels are powerfully

correlated with the class similarity, rise and fall

coherently and exhibiting fluctuation of a similar

shape under a subset of the gene conditions, called

the informative genes that participate in any

microarray data. The remaining gene data are

regarded as noise in the microarray data as they are irrelevant to the gene expression datasets. By

focusing on a subset of genes and the conditions of

significance, the noise levels are inducing by new


64

genes and condition can be lowered, which is

characterized by co-clustering. As a result, to identify

useful genes and decrease of gene dimensionality

used for clustering samples to detect their

substructure particular methods should be applied in

microarray datasets

Subspace clustering:

To find a subset of microarray data, such that the genes are appearing as a cluster in a subspace

produced by a subset of the gene expression data.

The subset of microarray data for different subspace

clusters can be different in a subspace clustering.

Genes and sample datasets are treated symmetrically,

such that each gene or sample genes can be regarded

as clusters or Biclustering features. A particular gene

may contribute in many Pathways that may or may

not be collective under all conditions Subspace

clustering techniques confine consistently exhibit by

the blocks within gene expression datasets.

K-means Clustering K-means clustering [6] is an easy and quick

method used commonly due to its simple

implementation and small number of genetic

expression datasets. The distance between every gene

and the center of every cluster is promptly calculated

to result in a grouping of gene data to clusters where

the genes inside all clusters are as close to the center

of the clusters as possible while at the equal point there is maximal distance connecting genes for

different clusters. This method is useful if different

values of k are attempted and it only gives the

amount of clusters not the relationship between them

like hierarchical clustering. The drawbacks of this

method are the lack of prior knowledge of the

number of gene clusters in a gene expression data

which results in the changing of consequences in the

altering of microarray data in successive runs because

the original clusters are selected randomly and the

quality of the clustering has to be assessed from the

gene expression datasets.

S.

N

O

AUTHOR CLUSTERING

TECHNIQUES

CLUSTERING

ANALYSIS

1. Erfaneh

Naghieh and

Yonghong

Peng[6]

K-means Clustering,

Hierarchical

clustering, Self-

Organised Maps

Clustering (SOM)

Gene based

clustering

2. LopamudraD

ey,

Anirban

Mukhopadhy

K means, FCM,

PSO, clustering

microarray gene

expression data.

Sample based

clustering

ay[11]

3. KA Abdul

Nazeer, MP

Sebastian &

SD Madhu

Kumar

Novel Harmony

Search-K means

Hybrid (HSKH)

algorithm.

Sub space

clustering

method

4. Adil M.

Bagirov

Karim

Mardaneh[2]

K means, global k

means algorithm

Gene based

clustering

method

Fuzzy c means clustering: In fuzzy, c means[5] clustering data

elements can belong to more than one cluster, and associated with each element is a set of membership

levels of the microarray data. They indicate the

strength of the association between that data element

and a particular cluster in the gene expression data.

Fuzzy clustering [12] is a method of assigning the

gene data membership levels in the clustering, and

then using them to allocate data elements to one

cluster or more clusters in the gene expression

datasets.

S.NO AUTHOR CLUSTERING

TECHNIQUES

CLUSTER

ANALYSIS

1. Matthias E.

Futschik and

Nikola K.

Kasabov[13]

Fuzzy c-means

(FCM)

clustering,

Robust analysis

of gene

expression

Time-series.

Sample

based

clustering

method

2. LopamudraDey,

Anirban

Mukhopadhyay[12]

K means, FCM,

PSO, clustering

microarray gene

expression data.

Gene based

clustering

method

3. Xlaobo zhou,

Xiaodong wang,

Eduward R.

Dougherty, Daniel

Ruls, Eduwadr

Shu[5]

Novel clustering

strategy using

fuzzy c means.

Subspace

and sample

based

clustering

4. Matthias E.

Futschik and

Nikola K.

Kasabov[14]

A robust

analysis of gene

expression

Time-series.

Gene based

clustering


65

Hierarchical clustering:

Hierarchical clustering [6] is the original and

mainly common clustering method applied to gene

expression data which is developed on the basis of a single layered neural network. A hierarchical

sequence of group of clusters is generated by

grouping genes with similar patterns of expression

across a range of samples located near the microarray

data to each other. Hierarchical clustering calculates

all pairs-wise space relationships between genes and

experiments to merge pairs of values that are most

similar to the formation of a node of gene datasets.

The inter-cluster distance groups together these

clusters to make a higher level cluster in the

microarray data, which can be graphically illustrated

by a tree, called a dendrogram representing the clusters and the relationship between genes and

microarray data. This is repeated, comparing genes or

new clusters until all clusters are joined in the

microarray datasets.

Advantages of hierarchical clustering: 1. Embedded flexibility concerning the level

of granularity in the genes.

2. Simplicity of behavior in any forms of similarity or distance from the microarray data.

3. Applicability to any attributes type of

clusters.

Disadvantages of hierarchical clustering 1. Ambiguity of termination criteria of the

gene clustering.

2. Generally hierarchal algorithms do not

return to once constructed clusters with the purpose

of gene expression datasets.

S.NO AUTHOR CLUSTERING

TECHNIQUES

CLUSTERING

ANALYSIS

1. Erfaneh

Naghieh and

Yonghong

Peng[6]

K-means

Clustering,

Hierarchical

clustering, Self-

Organised Maps

Clustering

(SOM)

Sample based

clustering

Self-Organized Maps Clustering (SOM): Self organized map clustering [16] is a

logically high-speed and simple to execute a method

in the microarray data, scalable to large gene

expression datasets. It is closely related to

multidimensional scaling and its genesis to represent

all microarray data in the basis space by points in a

target gene where the distance and nearness

relationships are preserved in the clustering. At the

input, the gene expression data are presented and

output genes are organized with an example

neighborhood gene structure in the clustering. The

significant features of SOM are that it generates a high dimensional microarray dataset and places

similar clusters near each other clusters in the genes,

so that the nearest clusters in this grid are more

related than clusters that are not nearest in the

microarray data. SOM [6] is trained through

competitive learning for the distribution of the input

data set which provides a relatively robust approach

than k-means in the clustering of highly noisy data.

However SOM requires users to input the number of

clusters and the grid structure of the neuron map.

After the completion of the training, Clusters are identified by mapping all data points to the output

gene expression datasets. The drawbacks of this

method is that it is not effective since the main

interesting patterns may be merged into only one or

two clusters and cannot be identified.

Particle swarm optimization: Particle Swarm Optimization (PSO)[14]

representation be first described in 1995, as a latest method for purpose microarray data. In PSO,

particles flow in virtual gene expression data are

looking for feasible solutions in the clustering, which

in our case are the same particles in the gene datasets.

Not like older separation of the clustering algorithms

where the number of partitions genes has to be

specified earlier to run in the microarray data, PSO

algorithm is able to forming clusters on its individual

depending on the level of similarity or dissimilarity

S

.

N

O

AUTHOR CLUSTERING

TECHNIQUES

CLUSTERING

ANALYSIS

1

.

Xiang Xiao,

Ernst R. Dow,

Russell

Eberhart, Zina

Ben Miled,

Robert J.

Oppelt[16]

The rate of

convergence is

improved by adding a

conscience factor to

the Self-Organizing

Maps algorithm

It is based on

the subspace

clustering

method

2

.

Erfaneh

Naghieh and

Yonghong

Peng[6]

K-means Clustering,

Hierarchical

clustering, Self-

Organised Maps

Clustering (SOM)

Gene and

sample based

clustering


66

between the gene expression datasets. The PSO K-

means algorithm [18] is really different from

SOM/PSO algorithm in the microarray data sense

that the latter uses PSO to optimize the weights of

gene expression data once it has been trained with

training microarray datasets.

Limitation in clustering of micro array data

It is meaningful to cluster genes and samples in a

gene expression dataset. Owing to this special characteristics of gene expression data, and the

particular requirements from the biological domain,

gene-based clustering face several new challenges

and is still an open problem. They are:

Owing to the complex procedures of microarray

experiments, gene expression data often contains

a huge amount of noise. Therefore, clustering

algorithms for gene expression data should be

strong to handle noisy data.

Overlapping gene clusters or sample clusters plays vital role in the biological function which

is a great challenge in clustering.

Users of microarray data may not only be

interested in the clusters of genes, but also

interested in the inter and intra relationship

between the gene clusters.

References:

[1] A. Abraham, C. Grosan and V. Ramos (2006) (Eds.),

Swarm Intelligence and Data Mining, Studies in

Computational Intelligence, Springer Verlag, Germany,

pages 270, ISBN: 3-540-34955-3.

[2] Adil M. Bagirov Karim Mardaneh, Modified global k-

means algorithm for clustering in gene expression data sets,

Australian Computer Society, Inc

[3] Ben-Dor, A., Shamir, R., and Yakhini, Z. 1999. Clustering

gene expression patterns. J. Comp. Biol. 6(3–4), 281–297.

[4] D. Jiang, C. Tang, A. Zhang, “Cluster Analysis for Gene

Expression Data: A Survey”, IEEE, vol. 16, no. 11, Nov.

2004.

[5] D. Dembele, and P. Kastner, “Fuzzy C-means method for

clustering microarray data”, Bioinformatics, 19, 973-980,

2003.

[6] E. Shay, “Microarray cluster analysis and applications”,

Available at:

http://www.science.co.il/enuka/Essays/Microarray-

Review.pdf, Jan, 2003.

[7] Eisen, M. Spellman, PL, Brown, PO, Brown, D. “Cluster

Analysis and Display of Genome-wide expression

patterns,” Proc. Natl. Acad. Sci. USA 95: 14863-14868,

1998.

[8] H. Turner, T. Bailey, W. Krzanowski, Improved

biclustering of microarray data demonstrated through

systematic performance tests, Comput. Stat. Data Anal. 48

(2) (2005) 235–254.

[9] J. C. Bezdek, R. Ehrlich and W. Full, “FCM: Fuzzy c-Mean

Algorithm”, Comp. and Geo-Science, 1984

[10] J. Liu, J. Yang, W. Wang, Biclustering in gene expression

data by tendency, in: Proceedings of the 2004

Computational Systems Bioinformatics Conference (CSB

2004), 2004, pp. 1–12

[11] K. E. Parsopoulos, M. N. Vra, “Particle Swarm

Optimization and Intelligence: Advances and

Applications”, Information science reference, Hershey,

New York, 2010.

[12] K. Premalatha, A. M. Natarajan, “A New Approach for

Data Clustering Based on PSO with Local Search”,

Computer and Information Science, Vol. 1, No.4,

November 2008.

[13] N.R.Pal, J.C.Bezdek,” On cluster validity for the fuzzy c-

means model”, IEEE Trans. on fuzzy systems, pp.370-379,

1995

[14] Qiang, N., Xinjian, H., “An Improved Fuzzy Cmeans

Clustering Algorithm based on PSO”, Journal of Software,

Vol. 6, No. 5, 2011.

S.

N

O

AUTHOR CLUSTERING

TECHNIQUES

CLUSTERING

ANALYSIS

1. Ajith

Abraham,

Swagatam

Das, and

Sandip

Roy[1]

A family of bio-

inspired

algorithms, well-

known as Swarm

Intelligence (SI)

Sample based

clustering

2. V. Kumutha,

S.

Palaniammal[

14]

Particle Swarm

Optimization, Gene

Expression Data,

Fuzzy c means

Algorithm.

Gene based

clustering

3. R.

Balamurugan,

A. M.

Natarajan, K.

Premalatha[1

8]

The Particle Swarm

Optimization (PSO,

have been analyzed

for the four

benchmark Gene

expression dataset.

It also based on

the subspace

clustering

method


67

[15] P.Törönen, M.Kolehmainen, G.Wong, E.Castrén,”

Analysis of gene expression data using self-organizing

maps”, FEBS Letters, Vol.451, pp. 142-146,1999

[16] S.C. Madeira and A.L. Oliveria, “Biclustering Algorithms

for Biological Data Analysis: A survey”, IEEE, vol. 1, no.

1, Jan- March 2004.

[17] K. Y. Yeung, W. L. Ruzzo. “Principal Component Analysis

for Clustering Gene Expression Data,” Bioinformatics,

17:763-774, 2001.

[18] R.Balamurugan A.M.Natarajan and K. Premalatha,

“Comparative Study on Swarm Intelligence Techniques for

Biclustering of Microarray Gene Expression Data.”, World

Acad. Sci. Eng. Technol., Int. J. Comput. Inf. Sci. Eng.

Vol.8, No. 2, pp. 4619-4625, 2014.

[19] T. Kohonen, Self-Organizing Maps, 3rd edition, New York:

Springer- Verlag, 2001

[20] Wolfgang Huber, Anja von Heydebreck, Martin Vingron,

Analysis of microarray gene expression data, April 2, 2003.

[21] X. Cui and T. E. Potok, "Document Clustering using

Particle Swarm Optimization", IEEE Swarm Intelligence

Symposium, Pasadena, California, 2005.

[22] Y. Cheng, G.M. Church. Biclustering of gene expression

data, in: Proceedings of ISMB 2000, 2000, pp. 93–103.


68

AUTOMATED AMBU BAG SYSTEM USED IN EMERGENCY SITUATION

Archana J 1 1*, Mary Helta Diasy 2

2

1, 2 St. Xavier’s Catholic College of Engineering, Nagercoil, India

ABSTRACT

AMBU stands for Artificial Manual Breathing Unit. Ambu bag otherwise called as manual resuscitator or self-inflating bag. It is hand held device used to provide positive pressure ventilation to a patient with breathing problem. Ambu bag is used for an artificial respiration device consisting of a bag that is squeezed by hand. Bagging is necessarily regular in medical emergencies when the patient’s breathing is insufficient and also to provide automated ventilation in preference to mouth to mouth resuscitation. In all the ambulances and in emergency wards of hospitals manually operated ambubag is used. The problems involved in this type of manual ambubag is (i) the negligence of the caretakers required quantity of oxygen is not carried over to the lungs, (ii) if the caretakers are alert one cannot assure that they expel a constant quantity of oxygen into the patient’s lungs and (iii) this type of mechanism basically is a stress to the caretakers. To overcome this problem, an automated ambu bag system is used. At the bottom of mechanical setup ambu bag is kept, cam mechanism is attached with the shaft of motor. While motor start rotating in clockwise direction cam mechanism will convert circular motion into

linear motion so that ambu bag will be compressed and expand and oxygen will be delivered to patient. Speed of the motor will be controlled by micro controller. Sensors are attached in this mechanical setup to monitor speed, pressure and oxygen flow to the patient. And speed will be displayed by LCD display. This automated ambu bag system is used for providing constant level oxygen to the needy patient.

Keywords: Ambu bag, DC motor, cam mechanism, pressure sensor, proximity sensor, LCD display and micro controller

I. INTRODUCTION

The human respiratory system allows one to obtain oxygen, eliminate carbon dioxide. Breathing consists of two phases, inspiration and expiration. Inspiration is the process of taking in air. Expiration is the process of blowing out air. Respiration rate varies according to age.

A resuscitator is a compact, portable device for on-the-scene, short term ventilator support and delivery of oxygen. It is used with a face mask and perhaps an oropharyngeal airway to keep the tongue from blocking the airflow. Also, an esophageal obdurator may be used to prevent the stomach from being inflated and to prevent regurgitation and aspiration of stomach contents. The bag resuscitator delivers a breath of oxygen to the patient when the operator squeezes the bag. Upon the release, the bag fills with oxygen again, while the patient exhales to the air through the inspiratory/expiratory (I/E) valve. The demand valve is essentially a simple artificial ventilator. It consists of a "smart"(I/E) valve coupled to an oxygen source through a flow resistance. The I/E valve is designed to open following the pressure drop of an inspiratory effort, after a built-in expiratory interval has elapsed, or when manually triggered by the operator. It remains open until a preset pressure is delivered. Two types of emergency resuscitators are shown in Figure 1.1(a) and 1.1(b) these ambu bag are varies according to the types of valve.

Other types of respiratory therapys are Gas-Delivery

Equipment, Vacuum-Delivery Systems and Suction

Devices, Humidifiers and Nebulizers, Intermittent Therapy

Equipment, Ventilators, Extracorporeal Oxygenators,

Monitoring Instrumentation, Neonatal Equipment, Patient-

Training Devices and Environmental Control Equipment.

Figure 1.1(a) Manual bag resuscitator and 1.1(b)

Demand valve

II. MATERIALS AND METHOD

Fig 2.1 shows the block diagram of the model, where

ambu bag is kept at the bottom for bagging operation.

Bagging will be done by cam mechanism. Speed of the

motor is controlled by PIC microcontroller. Proximity

sensor is used to sense the speed of the motor and that is

displayed on LCD. Pressure sensor will sense the pressure

of outlet air. And if the pressure exceeds it will produce the

alarm sound for indicating it. Heart beat sensor will

measure the heart rate of the patient at the same time. Switches are fixed for controlling the speed of the motor.

That is according to the age group the lung capacity will be

increased and the size of the ambu bag also increased.

According to the patient requirement the ambu bag must be

changed and exact button must be pressed. Incase if it is

adult, adult bag must be kept and adult button must be

pressed. Everything was controlled by microcontroller so

that ambu bag is compressed or expanded and oxygen is

delivered.


69

Fig 2.1 shows the block diagram of the system and the

various materials used are discussed below:

A. Ambubag:

Ambu bag otherwise called as Bag-valve-mask (BVM)

ventilation, it is an essential during emergency. This basic

airway management technique will allows for oxygenation

and ventilation of patients until more definitive airway can

be established and in some cases where endotracheal intubation or other definitive control of the airway is not

possible. For the emergency situation medical technician

will prefer this basic BVM ventilation for airway

management. In the pediatric population, BVM may be the

best preference for prehospital airway support. BVM

ventilation is also appropriate for elective ventilation in the

operating room when intubation is not required, but it is

now often replaced by the laryngeal mask airway.

The masks come in many sizes, including newborn, infant, child, and adult as the same way ambu bag size also

differ according to age. Choosing the appropriate size helps

to create a good seal and will provide effective ventilation.

Nowadays the bags are equipped with a pressure valve.

Some bags have one-way expiratory valves to prevent the

entry of room air; these allow for delivery of greater than

90% oxygen to ventilated and spontaneously breathing

patients. Bags lacking this feature deliver a high

concentration of oxygen during positive pressure ventilation

but only deliver 30% oxygen during spontaneous breaths.

B. PIC16F877A:

It has 100,000 erase/write cycle enhanced Flash

program memory. It is self-reprogrammable under the

software control. In-circuit serial programming via two pins

is possible. Low power, high speed Flash/EEPROM

technology is used here. In-circuit debugging via two pins

is possible. Programmable code protection can be done. It provides watchdog timer with its own on-chip RC oscillator

for reliable operation. It has up to 8-channel analog to

digital converter. It has wide operating voltage range.

C. Wiper Motor

This is a type of DC motor. The construction is single

speed motor. The armature with 8-slots is mounted on self-

lubricating sintered bushes. Two carbon brushes, set 180

degrees apart, rub on an 8 segment commutator generally

installed at the driving end. Two strong permanent magnets

are bonded to the steel yoke using an adhesive, which is

sometimes coated externally with non-ferrous metal to

protect it against corrosion. A steel worm, formed on the

end of the armature, drives a plastic worm wheel at a speed

of about l/10th the speed of the armature. The motor has the

output drive through a pinion gears, driven directly by the worm wheel. At the joint faces of the motor, rubber seals

are fitted to protect it from moisture. A polythene pipe is

used to vent the gases formed by arcing at the brushes.

Figure 2.2 Single-speed motor.

Typical values for wiper motor speed and hence wipe

frequency are 45 rpm and 65 rpm at normal and fast speed

respectively. The motor must overcome the starting friction

of each blade at a minimum speed of 5 rpm.

D. CAM

A ‘cam’ is a mechanical device with a surface or groove

that controls the motion of a second part called a ‘follower’

in order to convert rotary motion to linear motion.

Fig 2.3 Cam

Figure 2.3 shows the cam, this is specially designed cam.

At the bottom of cam, rubber ball is fixed to avoid the wear

and tear of the ambu bag.

E. Proximity sensors:

Proximity sensors is the metal sensing sensor. When

metal is approaching the sensor its output will be high.

When a no metal approaches the sensor its output will be

low.


70

F. Pressure sensor:

This is attached in the mechanical setup. It is used for

measure the pressure of expiratory air. Because the pressure

cause most dangerous situation. Patient may tend to vomit-

additional airway problems. To provide automated assist

device for breathing such that the oxygen supplied at

constant amount for the patients with pulmonary failure.

G. Heart beat sensor:

. Heart rate is calculated as the number of heart beats per

minute (beats/min). In the ECG, heart rate can be

determined by counting the number of P waves (atrial rate)

or QRS complexes (ventricular rate) in a specified time

period. One easy method involves counting the number of

QRS complexes in a 6-second period and multiplying that

number by ten. This gives the number of heartbeats (heart

rate) in 60 seconds. Six seconds in a standard ECG

comprises 30 large boxes (30 times 0.20 second per large square equals 6 seconds). Most ECGs have small vertical

hatched lines in the margins, which represent 3-second

intervals (6 seconds) and multiply by ten to determine the

heart rate per minute.

Fig 2.4 Block Diagram of Heart Beat Detection

III RESULT

Fig. 3.1 AutoCAD output

The above fig 3.1 shows the 3dimentional model of the

mechanical setup using AutoCAD software.

Fig. 3.2 Mechanical setup

As shown in fig 3.2 Cam mechanism is used and at the

end of cam rubber ball is fixed. When the motor starts

rotating in clockwise direction, it will compress and expand

the ambu bag. This rubber ball will prevents damage of

ambubag [9]. In this system we have the mechanical setup which has

wood plates at the bottom, over which the ambu bag is been

kept. The bagging operation is done with the help of cam

arrangement which is operated using DC shunt motor. The

speed of the DC motor is controlled by microcontroller

programming [7]. According to patient age (i.e) adults,

children, infants the output will be changed. Volume of air

will be different for adults, they need 12 to 16 breathing per

minute, for children they need 16 to 20 bpm, for infants 20

to 25 breathing per minute and the size of the ambu bag

varies according to the ages.

The motor speed is controlled by microcontroller. The PIC

Microcontroller board consists of circuits necessary to

operate a Microcontroller with PC interface. The board

contains provisions for interfacing 8 analog inputs and 23

Digital level signals [15].

IV. DISCUSSION

Totally 3 stepdown transformer is used. For operating

D.C motor 12 volt stepdown transformer is used. Another

12 volt transformer is used for giving the supply for all

sensors. And 5volt stepdown transformer is used for

microcontroller. ADC is used for converting analog signal

into digital signal. To activating pressure sensor AD620 IC

is used, and which needed supply of +12 and -12 volt. Driver circuit is also used.

V. CONCLUSION

This fabricated automated breathing device will have a

significant role in every ambulance and in hospitals. It may


71

possibly use for save the life of the patients who is suffering

from breathing disorders in a better manner when compared

with conventional type. This project provides automated

assist device for breathing such that the oxygen is supplied

at constant amount for the patients with pulmonary failure.

Hence vomiting or other complications do not occur using this automated type.

VI. REFERENCES

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[18]. Neil R. MacIntyre, MD 2006 “Current Issues in Mechanical

Ventilation for Respiratory Failure” Downloaded from

www.chestjournal.org at Taichung Veterans General Hospital.

[19]. Nguyen Van Tuong and Premysl Pokovny “A Case Study of

Modelling Concave Globaidal Cam” www.intechopen.com.

[20]. Olivier Legendre, 2012 “High-Resolution Micro-Pirani

Pressure Sensor With Transient Response Processing and Time-

Constant Evaluation” IEEE sensors journal, vol. 12, no. 10,

3090-3097.

[21]. Ramesh, G.Narasimharao, Robinson.A. 2010, 151-155.

Sathyabama Univ., Chennai, India , “Intelligent engine with

micro controller valve actuation and eliminating the cam

linkage arrangement” , Frontiers in Automobile and Mechanical

Engineering (FAME).

[22]. Riichiro Tadakuma 2013 “The Gear Mechanism with Passive

Rollers: The Input Mechanism to Drive the Omnidirectional

Gear and Worm Gearing” IEEE International Conference on

Robotics and Automation (ICRA).

[23]. Takeshi Takaki 2011,” High-Performance Anthropomorphic

Robot Hand With Grasping-Force-Magnification Mechanism”

IEEE/ASME transactions on mechatronics, vol. 16, no. 3,583-

589.

[24]. Yang Kaihua, 2012 “Research on Method of Fault Diagnosis

about Vehicle Wiper DC Motor” Intelligent System Design and

Engineering Application, Second International Conference.

[25]. Yunseog Hong, “Noncontact Proximity Vital Sign Sensor

Based on PLL for Sensitivity Enhancement” IEEE transactions

on biomedical circuits and systems1.

[26]. Zhenghao Ge,2010 “On the Hybrid Cam-linkage Mechanism

Realizing Variable Trajectory” International Conference on

Computer, Mechatronics, Control and Electronic Engineering

(CMCE), 272-276.


72

DISCRIMINATION OF SEMG SIGNALS BASED ON TEMPORAL AND

SPECTRAL APPROACH FOR FRAILTY ANALYSIS

Vidya K V1 and E. Priya

2*

1PG scholar, 2Assistant Professor

Department of Electronics and Communication Engineering, Sri Sai Ram Engineering College, Chennai, India

ABSTRACT Surface electromyography (sEMG) signal is the electrical manifestation of neuromuscular activities and depends on the anatomical

and physiological properties of the contracting muscles beneath the skin. In this work, a single channel surface EMG amplifier is designed to acquire the signals non-invasively from the skin by using bio-potential electrodes for three different hand movements. Subjects of age group 1 (18-30 years) and group 2 (60-78 years) are considered for this analysis. The recorded signals are pre-processed using Empirical Mode Decomposition (EMD) method to remove unwanted noises. Various error measures and performance index are calculated from the pre-processed sEMG signals to compare the performance of EMD with conventional digital filters. Relevant time and frequency domain features are extracted from the pre-processed signals. It is observed that the statistical analysis performed over the extracted features show

distinct variation between the age groups. Further, the classification of hand movements is done for both age groups independently using linear discriminant analysis classifier. Results demonstrate that training and testing accuracy for age group1 are 94.44% and 80.64% and that for age group2 are 93% and 76.47% respectively. Thus the methodology proposed in this work could be useful for the analysis of frailty especially for the subjects above 60 years.

Keywords: surface electromyography signal; empirical mode decomposition; digital filters; temporal and spectral features; linear discriminant analysis

I. INTRODUCTION

Frailty is a complex phenomena associated with ageing and has become a main area of research in gerontology. Frailty can be recognized by frailty phenotype and frailty index [1]. The criteria used for frailty analysis are weight loss, reduced endurance, physical slowness, muscle weakness and physical exhaustion. An individual is termed frail if at-least three of these conditions are satisfied. This work uses surface Electromyography (sEMG) signals for comparative analysis of young with elderly subjects. The sEMG signal is an electrical signal produced as a result of muscular exertion. This signal is recorded non-invasively by applying conductive elements or electrodes to the skin surface. Since sEMG signals detect the electrical activity associated with muscular exertion, they reveal important neural changes and decline in physical functions associated with ageing [2].

The sEMG signal is a very weak signal ranging from 20–500 Hz and is affected by number of noises [3-5]. Its amplitude is stochastic (random) in nature making it difficult to get the intrinsic properties of these signals from a single feature. Also the instantaneous value of the sEMG signal is not useful because of its random nature [6]. So the sEMG signals are segmented to form windows from which features are extracted. Allowable window length ranges from 50 to 400ms and the optimal window length is between 150ms and 250ms [7, 8].

Several signal analysis approaches have been reported in literature to characterize the sEMG signals [2, 9-12]. The sEMG signal analysis have wide range of applications such as prosthetic devices control, human machine interaction, functional electrical stimulation, kinematic parameter prediction, neuromuscular disease diagnosis and so on [13-16]. EMG measurements are used for the control of

prosthetic devices like artificial limbs. This involves picking up of EMG signals from the terminated nerve endings and activating an artificial arm using these signals [17].

Feature extraction is an important step in the sEMG signal processing and the classification accuracy depends mainly on the choice of feature set [17]. Waveform length (WL), time windowed Root Mean Square (RMS), Auto Regression (AR) coefficients, cepstrum coefficients, Mean Absolute Value (MAV), MAV slope, Slope Sign Changes (SSC) and zero crossings (ZC) are the useful time domain features. The common frequency domain features include mean and median frequency and spectral moments [5, 7, 16, 18].

Linear Discriminant Aanalysis (LDA) classifier has high computational efficiency for real-time operation. It is simple and has classification performance similar to more complex algorithms [18].


A. EMG signal acquisition system

In this work, a single channel sEMG amplifier circuit is developed to acquire the signal. The EMG signal acquisition system consists of the important blocks shown in Fig. 1.

Fig. 1. Block diagram of EMG signal acquisition system

* [email protected]

Electrodes EMG

amplifier

Analog to

digital

converter

PC


73

Fig. 2. Different hand movements used in this work

The sEMG signal is acquired from two age groups, age group 1 (18-30 years) and age group 2 (60-78 years).

Twenty volunteers, including seniors (with an average age

of 70 years) and young people (with an average age of 23

years) are recruited for this work. The subjects are asked to

perform a ‘rest-motion-rest’ pattern for three different hand

movements. The hand movements chosen are closed fist,

spherical grasp and pointing gesture as shown in Fig. 2. The

subjects are instructed to perform five repetitions of each hand motion and are allowed to rest for one minute before

performing the next hand movement.

The disposable surface electrodes are secured over the relevant muscles on the body surface, after initial skin

preparation. These electrodes pick up the voltages produced

by the contracting muscle. A single channel sEMG

amplifier consists of three electrodes, of which two

electrodes are placed over the relevant muscle and the third

act as the reference electrode. The reference electrode is

placed on the wrist and the signal electrodes on the flexor

digitorum superficialis muscle [6].

Low frequency components in the sEMG signal are

mainly noise components. Once the sEMG signals are

acquired using the pre-amplifier, RC high pass filter with a

cutoff frequency 12 Hz is used. This removes most of the

movement artifacts and low frequency noises. The high

pass filter is followed by a second stage of amplification for

bringing the signal to transistor transistor logic level. This

stage is designed to give an adjustable gain with a maximum of 20 times amplification to the filtered signal.

The bias adjustment is provided to resolve offset problems

in the amplified signal. This is used to change the reference

level of the signal.

The output of the EMG amplifier is given to the Analog to Digital Converter (ADC) whose resolution is of 10 bits.

The sampling frequency chosen in this work is twice the

highest frequency (Nyquist rate) and thus the signals are

sampled at 1000 Hz. The amplified and filtered sEMG

signals are sampled and recorded using LabView program

in PC for further processing.

B. EMG signal processing and analysis

Fig. 3. Block diagram of sEMG signal processing

The raw sEMG signal obtained after ADC conversion is pre-processed using EMD algorithm. The frequency

components below 10 Hz are removed after the

decomposition procedure. The sEMG signal is then

reconstructed with the frequency components 20-500 Hz.

The functional blocks of sEMG signal processing are

shown in Fig. 3.

Pre-processing is a crucial and essential step in sEMG

signal processing. One of the methods of pre-processing is

by using conventional digital filters but they distort the

filtered sEMG signal after pre-processing. In this work,

EMD algorithm is used as the pre-processing technique. EMD is able to deal with non-stationary and non-linear

signals thus making it suitable for sEMG signals. EMD is a

purely data driven, signal-dependent procedure and makes

no assumptions about the input signal [19].

EMD is a technique which decomposes a given signal

into a finite number of one dimensional functions called

Intrinsic Mode Functions (IMFs). Given any signal ( )x t ,

the IMFs are found by an iterative procedure called sifting

algorithm. EMD algorithm involves finding all the local

maximai

M , for 1,2,...i and minimak

m , for

1,2,...k in ( )x t and are interpolated as

( ) ( , )M i

M t f M t and ,m k

m t f m t .

Pre

-pro

cess

ing

Decomposition using EMD

algorithm

Removal of very low

frequencies

Feature extraction

Reconstruction of the signal

after noise removal

Raw sEMG

signal

Classification


74

These interpolated signals form the upper and lower

envelopes of the signal. The mean of these envelopes are

calculated as

2

M t m te t

and is subtracted from

the original signal as x t x t e t . The procedure is

iterated until x t remains nearly unchanged forming an

IMF. The obtained IMF t is then subtracted from the

signal x t x t t . The whole process is repeated

if x t has more than one extremum (neither a constant nor

a trend).

Large baseline wandering components are present in

higher order IMFs. Thus the de-noised signal is

reconstructed with the lower order IMFs leaving the three higher order IMFs. This reconstructed signal is used for

extracting both time and frequency domain features [20].

The performance of EMD is compared with

conventional infinite impulse response causal and non-

causal filters. Error measures such as Mean Square Error

(MSE), Root Mean Square Error (RMSE), Mean Absolute

Error (MAE), Relative Error (RE) and performance index

measure, Signal to Error Ratio (SER) are used for

validating the EMD algorithm. The mathematical

formulations are presented in Table 1, where 0 ( )x t is the

original, ( )fx t is the pre-processed signal defined for N

samples, ( )o

P f and ( )f

P f are the spectral density of

original and pre-processed signal [21].

TABLE I. MATHEMATICAL FORMULATIONS OF ERROR

MEASURES AND PERFORMANCE INDEX

Performance

measures Formula

MSE 2

1

1( ) ( )

N

o f

i

x t x tN

RMSE 2

1

1( ) ( )

N

o f

i

x t x tN

MAE

1

1( ) ( )

N

o f

i

x t x tN

RE

2

2

( ) ( )

( )

o f

o

P f P f

P f

SER

var

10 logvar

o

o f

x t

x t x t

C. Feature extraction

Feature extraction delivers useful information from

the signal. Mean absolute value gives the mean of absolute

value of signal x in a time window with N samples. It is a

simple measure to detect muscle contraction levels [22].

Mean Absolute Value (MAV) is formulated as

1

1 N

k

k

MAV xN

(1)

where kx is the thk sample in the analysis window.

Integrated Absolute Value (IAV) is defined as

1

N

k

k

IAV x MAV N

(2)

IAV is used as an onset index to detect muscle activity and

to measure total muscular effort [23]. RMS is expressed as

2

1

1 N

k

k

RMS xN

(3)

RMS is a measure of the power of sEMG signal that is

related to constant force and non-fatiguing contraction [3], [18]. Waveform length is defined as the cumulative length

of the EMG signal within the analysis window. WL

provides a measure of the complexity of the signal [24].

1

1

where

N

k k k k

k

WL x x x x

(4) (4)

Auto Regression coefficients provides information about the muscle's contraction state. AR models individual EMG

signals as a linear autoregressive time series. It is defined as

1

p

k i k i k

i

x a x e

(5)

where ia represents auto regressive coefficients, p is the

order of AR model, and ke is the residual white noise.

Zero crossing is the number of times signal x crosses

zero within an analysis window. It is associated with the

frequency of the signal. To avoid signal crossing counts due

to low-level noise, a threshold is included. The ZC count

is increased by 1 if

1 1

1

and 0 or and 0

and

k k k k

k k

x x x x

x x

(6)

Median Frequency, MDF is a frequency at which the EMG

power spectrum is divided into two regions with equal

amplitude. It can also be considered as half of the total

power. The definition of MDF is given by

1 1

1

2

MDF M M

j j j

j j MDF j

P P P

(7)


75

Spectral moments are the alternative possibility to extract

the features from a power spectral density and are defined

as

0

, 0,1,...,

W

m

mM f P f df m L (8)

Equation (5) defines the spectral moments derived from

power spectral density for a bandwidth W. The first three

moments of PSD are 0 1 2, and MM M

D. Classification

Classification of the three hand movements is done for

age group 1 and 2 independently after the extraction of

relevant features. The LDA classifier is used for the

classification in this work. Out of the total samples in each

hand movement, features from 70% of the samples are used as training data and the rest is used for testing. The

procedure is done for both age groups separately.

In LDA, three scatter matrices, called the within-class

wS , between-class bS , and total scatter tS matrices are

computed. It involves computing the eigen values ( )i of

tS so that1

dT

t i i i

i

S u u

. Then the eigen vectors ( )iu of

t bS S is computed where tS

denotes the pseudo inverse

of tS and 1

dT

t i i i

i

S u u

[25].

III. RESULTS

The raw sEMG signal acquired using the sEMG amplifier for group 1 and 2 are shown in Fig. 4 (a) and (b)

respectively. The signal shows a single burst acquired from

‘rest-motion-rest’ action and an offset shift in voltage.

The corresponding power spectrum density is presented

in Fig. 5 (a) and (b) respectively. The plot shows that the

dominant energy component of sEMG lies within the

frequency range 50-150 Hz. Distinct difference is observed

between the age group 1 and 2 indicating the onset of frailty

in elderly.

0 200 400 600 800 10000

1

2

3

4

5

time (ms)

sEM

G a

mpl

itude

(V

)

(a)

0 200 400 600 800 10000

1

2

3

4

5

time (ms)

sEM

G a

mpl

itude

(V)

(b)

Fig. 4. sEMG signal of age (a) group 1 and (b) 2

0 100 200 300 400 500

0

1

2

3

4

5

6x 10

-3

Pow

er/

Hz

Frequency (Hz) (a)

0 100 200 300 400 5000

1

2

3

4

5

6x 10

-3

Pow

er/H

z

Frequency (Hz) (b)

Fig. 5. Power spectral densities of age (a) group 1 and (b) 2

The sEMG signals are further decomposed by EMD

algorithm to de-noise the signal.

0 100 200 300 400 500 600 700 800 900 1000

IMF 1

IMF 2

IMF 3

IMF 9

IMF 8

IMF 7

IMF 6

IMF 5

IMF 4

IMF10

IMF11

(a)


76

0 100 200 300 400 500 600 700 800 900 1000

IMF 1

IMF 10

IMF 11

IMF 9

IMF 2

IMF 3

IMF 4

IMF 5

IMF 6

IMF 7

IMF 8

(b)

Fig. 6. sEMG signal decomposition into IMFs for age group (a) 1and (b) 2

The decomposed signals with eleven IMFs are shown in

Fig. 6 (a) and (b). The three higher order IMFs are excluded

to reconstruct the pre-processed sEMG signal.

Fig. 7 (a) and (b) shows the reconstructed sEMG signal

with offset being nullified and their corresponding power

spectral density is presented in Fig. 8 (a) and (b). Plots

show that the low frequency (noisy) components are

reduced after EMD pre-processing.

0 200 400 600 800 1000-3

-2

-1

0

1

2

3

time (ms)

sEM

G a

mpl

itude

(V)

(a)

0 200 400 600 800 1000-3

-2

-1

0

1

2

3

time (ms)

sEM

G a

mpl

itude

(V)

(b)

Fig. 7. Reconstructed sEMG signal of age group (a) 1and (b) 2

0 100 200 300 400 5000

1

2

3

4

5

6x 10

-3

Pow

er/H

z

Frequency (Hz)

(a)

0 100 200 300 400 5000

1

2

3

4

5

6x 10

-3

Pow

er/H

z

Frequency (Hz)

(b)

Fig. 8. Power spectral densities of reconstructed sEMG signal of age group

(a) 1and (b) 2

The EMD algorithm is validated by comparing with

conventional digital filters. Table 2, 3 and 4 shows the mean, standard deviation of the error measures and

performance index, SER. The comparison across the age

group is also presented in the table.

TABLE 2. COMPARISON OF ERROR MEASURES, MSE AND RMSE

VALUES (MEAN ± SD) FOR EMD AND CONVENTIONAL DIGITAL

FILTERS

Performance index MSE RMSE

Gro

up

1

IIR causal 0.092 ± 0.047 0.297 ± 0.071

IIR non-causal 0.0062 ± 0.004 0.075 ± 0.026

EMD 0.003 ± 0.003 0.049 ± 0.026

Gro

up

2

IIR causal 0.074 ± 0.03 0.267 ± 0.061

IIR non-causal 0.004 ± 0.001 0.061 ± 0.008

EMD 0.002 ± 0.0005 0.039 ± 0.007

TABLE 3. COMPARISON OF ERROR MEASURES, MAE AND RE

VALUES (MEAN ± SD) FOR EMD AND CONVENTIONAL DIGITAL

FILTERS

Performance

index MAE RE

Gro

up

1

IIR causal 0.211 ± 0.054 0.005 ± 0.02

IIR non-causal 0.059 ± 0.021 0.004 ± 0.02

EMD 0.043 ± 0.023 0.00052 ± 0.0006

Gro

up

2

IIR causal 0.195 ± 0.051 0.005 ± 0.003

IIR non-causal 0.048 ± 0.006 0.0037 ± 0.002

EMD 0.033 ± 0.004 0.0008 ± 0.0009


77

TABLE 4. COMPARISON OF ERROR MEASURES, SER VALUE

(MEAN ± SD) FOR EMD AND CONVENTIONAL DIGITAL FILTERS

Performance index SER

Gro

up

1

IIR causal 2.474 ± 0.64

IIR non-causal 15.761 ± 1.102

EMD 23.557 ± 3.171

Gro

up

2

IIR causal 2.066 ± 1.432

IIR non-causal 15.853 ± 2.56

EMD 23.372 ± 2.43

It is observed from Table 2, 3 and 4 that the error measures are found to be lower with higher SER value for

EMD based pre-processing when compared to IIR causal

and non-causal filters.

The line plot shown in Fig. 9 (a), (b) presents the average values of MAV, RMS, M0 and Fig. 9 (c), (d)

presents IAV and WL at ‘rest-motion-rest’ for age group 1

and 2 respectively.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Zeroth Spectral Moment

Mean Absolute Value

Root Mean Square

Ave

rag

e V

alu

es

rest max contraction rest

Group 1

hand movement (Closed fist)

(a)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Zeroth Spectral Moment

Mean Absolute Value

Root Mean Square

Ave

rag

e V

alu

es

rest max contraction rest

Group 2


(b)

20

40

60

80

100

120

Group 1


max contraction restrest

Ave

rag

e V

alu

es

Integral Absolute Value

Waveform Length

(c)

20

40

60

80

100

120

Group 2


max contraction restrest

Ave

rag

e V

alu

es

Integral Absolute Value

Waveform Length

(d)

Fig. 9. (a), (b) MAV, RMS, M0 and (c), (d) IAV, WL of age group 1 & 2

for closed fist hand motion

Fig. 10 (a)-(d) shows the values for maximum contractions

for the other two hand motions. It is observed that MAV,

RMS, zeroth spectral moment (M0), IAV and WL are

higher for young (group 1) compared to elderly (group 2).

0.0

0.1

0.2

0.3

0.4

0.5

Group 1

Group 2V

alu

e fo

r m

ax c

on

trac

tio

n

MAV RMS M0 (a)

0

10

20

30

40

50

60

70

80

Group 1

Group 2

Va

lue

s f

or

ma

x c

on

tra

cti

on

IAV WL (b)

0.0

0.1

0.2

0.3

0.4

0.5 Group 1

Group 2

Val

ues

fo

r m

ax

co

ntr

acti

on

MAV RMS M0


78

(c)

0

10

20

30

40

50

60

70

80

Group 1

Group 2

Va

lue

s f

or

ma

x c

on

tra

cti

on

IAV WL

(d)

Fig. 10. (a) , (c) MAV, RMS, M0 and (b), (d) IAV, WL of age group 1 &

2 for spherical grasp and pointing gesture

Table 5. Feature number and names notations

Feature

numbers

Feature

names

1 M0

2 M1

3 M2

4 MAV

5 RMS

6 WL

7 AR

8 MDF

9 ZC

Table 5 shows the numerical notations for different features that are used in Table 6. Table 6 shows the

classification accuracies of age group 1 and 2 for different

combinations of features.

Table 6. Classification accuracy of age group 1 and 2 for different feature

combinations

Features Classification accuracy (Percentage)

Age group1 Age group2

Training

accuracy

Testing

accuracy

Training

accuracy

Testing

accuracy

1 50 45.16 44 35.29

1,2 56.94 45.16 32 35.29

1,2,3 72.22 74.19 72 47.05

1,2,3,4 75 61.29 76 58.82

1,2,3,4,5 77.77 54.83 77 55.88

1,2,3,4,5,

6 79.16 61.29 92 55.88

1,2,3,4,5,

6,7 86.11 77.41 92.12 67.64

1,2,3,4,5,

6,7,8 93.05 80.64 92.4 76.47

1,2,3,4,5,

6,7,8,9 94.44 80.64 93 76.47

IV. DISCUSSION

Frailty is a phenomena associated with ageing. The

musculoskeletal system declines as normal ageing

progresses. The reduction in muscle mass and muscle

function is one of the outcomes of this decline [26]. The

sEMG signals are electrical manifestation of the

neuromuscular activation due to muscle contractions and,

can reveal decline in physical functions associated with

frailty [2]. There is no conclusive work reported in

literature dealing with EMD pre-processing in frailty analysis. This work also compares the performance of EMD

across age-groups.

The power spectrum density of the raw sEMG signals

(Fig. 4 (c) and (d)) shows that the dominant energy

components of sEMG are within the frequency range 50-

150 Hz. Also the power of the sEMG signal for elderly is

found to be lower compared to the young. It is observed

from the power spectral density of the reconstructed signal (Fig. 6 (c) and (d)) that the noises at lower frequencies are

considerably reduced after EMD. Also the reconstructed

signal is devoid of the offset voltage. The EMD algorithm is

validated by comparing with conventional digital filters

(Table 2, 3 and 4). The error measures are found to be

lower with higher SER value for EMD based pre-

processing when compared to causal and non-causal filters.

Error measures and SER for group 1 and group 2 are found

to be similar. This shows that EMD is able to eliminate

baseline wandering artifacts from the sEMG signals

irrespective of the age group making it a suitable pre-processing technique for sEMG signals.

MAV, IAV, RMS and WL are features that are

directly related to force of muscle contractions and the

lower values of these features in age group 2, could be due

to the weaker muscle contractions in elderly. The lower

values of moments of power spectral density and their

gradual variation with the muscle contraction in elderly

could be due to age related decrease in the number and size of fast twitch fibers in elderly. Results demonstrate that the

above observations are consistent for all the three hand

movements.

The classification of hand movements done for

both the age groups separately. The table shows that both

the testing and training accuracies are lower in age group 2.


79

V. CONCLUSION

It can thus be concluded that the lower sEMG

signal power in elderly is due to lower muscle contraction

and muscular effort. The MAV, IAV, RMS and WL in time

domain and spectral moments in frequency domain are

more relevant features that depict the frailty analysis

between the two age groups. Thus the methodology used in

this work appears to be suitable for analysis of muscle

weakness which is one of the most significant characteristics of frailty in elderly.

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80

MODEL IDENTIFICATION AND CONTROLLER DESIGN OF A PERFUSION

SYSTEM DURING AN ECMO SUPPORT

M.Dhinakaran 1*, Dr S.Abraham Lincon

2, P.Praveen Kumar

3

1Assistant Professor, Department of Instrumentation Engineering, Annamalai University, Tamil Nadu, India.

2Professor, Department of Instrumentation Engineering, Annamalai University, Tamil Nadu, India.

3M.E.Student, Department of Instrumentation Engineering, Annamalai University, Tamil Nadu, India.

ABSTRACT Extracorporeal Membrane Oxygenation (ECMO) is a temporary life support system used for patients who’s Heart or Lungs is not

working properly. ECMO is a modified form of Heart Lung Machine and it can be used for a longer period. ECMO system must be managed by a Perfusionist to maintain proper Blood flow and Blood pressure. Much of the time, the Perfusionist makes small adjustments in the ECMO system to maintain flow and pressure. This maintenance process can be tedious and is prone to human error. So by the introduction of an Automatic control in an ECMO system the variables can be perfectly controlled. In this work model identification and

controller design of a perfusion system done by real time BGA Reports collected from Various Cardiopulmonary bypass surgery Patients. For that an identified model is estimated and tested in MATLAB System Identification Toolbox.The Proposed Controller is designed and tested in MATLAB Simulink for an ECMO conditions. So this control strategy presented improves the patient’s safety.

Keywords: Extracorporeal Membrane Oxygenation ECMO, Blood Gas Analyzer BGA, Partial Pressure of Oxygen (pO2),

Partial Pressure of Carbon dioxide pressure (pCO2).

I. INTRODUCTION

Extracorporeal membrane oxygenation (ECMO) is a

unique form of cardiopulmonary bypass. It has become

standard therapy in the management of neonatal respiratory

failure in the past decade, and is also being used selectively

in the support of respiratory and cardiac failure in the

pediatric and adult populations.

Fig. 1. Blockdiagram of an ECMO Setup

The first adult ECMO was used by Hill for aortic

rupture in the year 1972 and the first report of successful

ECMO support was applied by Robert Bartlett to a newborn

with respiratory failure in the year 1975.The extracorporeal

membrane oxygenation (ECMO) apparatus consists of a venous reservoir, blood pump with tubing, a membrane

oxygenator, and a countercurrent heat exchanger. The blood

pump is either a simple roller pump (most common) or a

constrained vortex centrifugal pumps. The oxygenator is

responsible for exchanging both oxygen and carbon

dioxide. Three types of commercial oxygenator are

available are membrane oxygrnator, bubble oxygenator, and

hollow-fibre oxygenator. The heat exchanger used to warm

the blood by exposing to warm water that circulates within

metal tubing.

II. ECMO SETUP

ECMO support currently comes in two varieties:

VenoArterial (VA) ECMO, and VenoVenous (VV) ECMO.

VA ECMO - It is used to support both heart and lungs

function. Here a cannula is placed through the right jugular

vein into the right atrium.The Deoxygenated Blood is

passed to a venous reservoir and pumped by a pump

through the oxygenator. In oxygenator gas exchange occurs

by oxygen added to the blood and carbon dioxide is

removed, next, the blood is warmed by the heat exchanger

to a body temperature before returning to the patient through a cannula placed through the right carotid artery

into the aorta.

VV ECMO - It is used to support lungs function. Here a

double-lumen cannula is placed through the right jugular

vein into the right atrium. Deoxygenated blood is

withdrawn from the right atrium through the outer

fenestrated venous catheter wall, and through the inner


81

lumen of the catheter the oxygenated blood is returned

across the tricuspid valve.

The VAECMO and VV ECMO cannula placement in Heart

is shown in Figure2

Fig. 2. Types of ECMO

III. ECMO MONITORIG AND CONTROL

ECMO Process Monitoring and Control done by the

coordination between perfusionist and anesthetist,

Fig. 3. Chid placed in a VAECMO setup

ECMO Monitoring and Measurement Includes: Perfusion pressure, venous return, Urine output, Temperature, Blood

gas, Electrolytes, ACT (Clotting time),Partial pressure of

Oxygen PO2 and Partial Pressure of Carbon dioxide

PCO2,ECG, EEG, Pump speed etc

ECMO Important Control Includes:

PO2 - obtained by varying the combination of FiO2 and

Medical air

PCO2 - Obtained by Blood/Gas Flow

Hemoglobin saturation - achieved by the increase in blood

flow or Hemoglobin Concentration

Anticoagulation - Obtained by increase in Heparin level Platelets - Achieved by Platelets Transfusion

Oxygen uptake -Varied by changing the temperature

Etc..

IV. BLOOD GAS ANALYZER

The major function of the pulmonary system is to

deliver oxygen to cells and remove carbon dioxide from the

cells. Blood Gas Analyzer tests are performed to assess

respiratory status because it helps to evaluate gas exchange

in the lungs. BGA test can also measures the working

condition of lungs and kidneys. Blood gas analyzer present

in JIPMER Hospital Puducherry is shown in Figure 4.

Fig. 4. Blood Gas Analyzer (Model RapidLab-348)

BGA Performs the diagnostic test on blood drawn from

venous side or arterial side. Some of the components

measured by BGA are PH: measures hydrogen ion concentration in the blood, it

shows acidity or alkalinity of blood.

(Normal range - 7.35 to 7.45)

PCO2: It is the partial pressure of CO2 that is carried by the

blood for excretion by the lungs, known as respiratory

parameter.

(Normal range - 35to 45 mmhg)

PO2: It is the partial pressure of O2 that is dissolved in the

blood and it reflects the body ability to pick up oxygen from

the lungs

(Normal range - 80 to 100 mmhg)


82

HCO3: it reflects the kidney’s ability to retain and excrete

bicarbonate, known as the metabolic parameter,

(Normal range - 22 to 28 meq/L)

V. REAL TIME DATAS OF CPB SURGERY PATIENTS

Various CPB Surgery Patients BGA Reports are

collected . This arterial blood gases report is measured for

every fifteen seconds with an ECMO support. The input

level and output level simultaneously changes, which

depends on patients age and their physical conditions. This

patients ABGA report is completely analyzed by

comparing with past recovered patients records.

TABLE I. AVERAGE OF BGA PATIENTS REPORTS COLLECTED

VI. PATIENT DATA ESTIMATION USING SYSTEM

IDENTIFACTION TOOL BOX

For the Average patient data and with the suggestions

from perfusionist and Specialzed Doctors’ a strong process

model at various time delay is designed by using system

identification toolbox in matlab environment.

Model Identification of Patients Data: Initially ten patients

ABGA Report is analyzed and its average taken. This

average data is used for designing the linear model of the

system with the system identification toolbox in MatLab

environment.

Inspired oxygen gases FiO2and pressure of oxygen pO2 can

be modeled as the linear transfer function. The Oxygen

Plant is given as

)1( 059.177.1

2

3322.735.59

2)(

SS

S

OsG

Total blood flow rate and pressure of carbon dioxide pCO2

can be modeled as the linear transfer function. The Carbon

dioxide Plant is given as

)2( 059.1S77.1

2S

08023.0S087.3

2CO)s(G

VII. CONTROLLER DESIGN

The Conventional Controller is used for Controlling

CPB Gases. So here the PID Controller is chosen as a

Conventional Controller . The PID controller takes the present, the past, and the future error into considerations.

Fig. 5. Block diagram of Control mechanisms of the CPB Gases

PID controller is constructed from various combinations

of proportional, integral and derivative in terms of patients

Number

of

Patients

Input Output

FiO2

[%]

Blood

gases

Flow[LPM]

pO2

[mmHg] pCO2

[mmHg]

Patients 1

42.22222 1.780556 316.825 40.9

Patients2 48.61111 1.620278 312.0833 43.2

Patients 3 42.22222 1.780556 316.825 40.9

Patients 4 47.33333 1.652333 313.2688 42.63

Patients 5 42.05556 1.692389 315.6396 41.48

Patients 6 42.08333 1.707083 315.8767 41.36

Patients 7 44.13889 1.732472 315.442 41.58

Patients 8 45.05556 1.686389 314.4542 42.05

Patients 9 44.08796 1.705532 315.0864 41.75

Patients 10 44.09524 1.709381 315.1372 41.72


83

measured BGA report model which is required to meet

specific performance requirement functions. The General

formula of the PID Transfer function given as

)3( d

K S

iK

pK)s(GC

Controller selection:

ECMO Support : The PI Control mode is selected

CPB Surgery : PID Control mode is selected

VIII. PID CONTROLLER RESPONSES OF CPB GASES

DURING AN ECMO SUPPORT

0 100 200 300 400 500 600 700 800 9000

50

100

150

200

250

300

350

Time (sec)

Pressure of O

xygen (pO

2)

pO2

Fig. 6. Partial pressure of oxygen during an ECMO

Support

0 100 200 300 400 500 600 700 800 9000

10

20

30

40

50

60

Time (sec)

Pre

ssure

of

Carb

an d

ioxid

e (

pC

O2)

pCO2

Fig. 7. Partial pressure of Carbon dioxide during an ECMO Support

IX. PID CONTROLLER RESPONSES OF CPB GASES

DURING CPB SURGERY

0 20 40 60 80 100 120 140 160 180 2000

50

100

150

200

250

300

350

pO

2 [

mm

Hg

]

Time (Sec)

PID Controller Responses for CPB Gases

Fig. 8. Partial pressure of oxygen during CPB Surgery

0 20 40 60 80 100 120 140 160 180 2000

5

10

15

20

25

30

35

40

45p

CO

2 [

mm

Hg

]

Time [Sec]

PID Controller Responses for CPB Gases

Fig. 9. Partial pressure of Carbon dioxide during CPB Surgery

X. CONTROLLER RESULTS

TABLE II. CONTROLLER PARAMETERS FOR AN ECMO

SUPPORT

Controller Parameters pO2 pCO2

Proportional Gain (Kp) 0.017 3

Integral Gain (Ki) 6.18 9


84

TABLE III. CHARACTERISTICS OF CONTROLLERS FOR

ECMO SUPPORT

Controller Characteristics pO2

[mmHg]

pCO2

[mmHg]

Maximum Saturation limit 600 55

Set Point (mmHg) 315 45

Over shoot (%) 13.5 13.24

Settling Time (sec) 80 10

Rise Time (sec) 35 2.5

TABLE IV. CONTROLLER PARAMETER FOR CPB

SURGERY

Controller Parameters pO2 pCO2

Proportional Gain (Kp) 0.69 1.23

Integral Gain (Ki) 2.65 0.04

Derivative Gain (Kd) 0.0084 -0.17

TABLE V. CHARACTERISTICS OF CONTROLLERS

FOR CPB SURGERY

Set point

pO2

mmHg

Time (sec) Controller Characteristics

min max

Over

shoot

(%)

Rise

time

(sec)

Settling

Time

(sec)

150 0 15 5.3 0.5 2.7

280 15 30 5.1 15.6 17.2

300 30 45 3.2 30.4 31.9

320 45 60 2.7 45.4 46.6

310 60 75 2.1 60.2 62.1

300 75 90 2 75.2 76.2

250 90 105 1.8 90.3 91.8

300 105 120 1.5 105.2 106.2

320 120 135 1.4 120.3 121.1

300 135 150 0.7 135.2 136.09

TABLE VI. CHARACTERISTICS OF CONTROLLERS

FOR CPB SURGERY DISCUSSION

Real time BGA Reports collected from Various

Cardiopulmonary bypass surgery Patients. For FiO2 and

Partial pressure of oxygen PO2, the Blood Gases flow and

Partial pressure of carbon dioxide PCO2 is Identified into

second order model and it is developed in MATLAB

System Identification Toolbox. The Proposed Controller is

designed and tested in MATLAB Simulink for an

ECMO/CPB Surgery conditions.

XI. CONCLUSION

The main proposal of this research is to analyze the Real

time BGA Reports collected from Various Cardiopulmonary

bypass surgery Patients and Controller Characteristics are

obtained for ECMO conditions. This controller

characteristics can used in future developments of robustic

algorithm for an ECMO support.

Acknowledgment

We wish to express our sincere thanks to Mr Mathavan

Senior Perfusionist CTVS Department JIPMER Hospital

Pudhucherry and Mr Yogesh Perfusionist (Heart Lung

Machine) KMCH Hospital Erode. Who provide us valuable

guidance and timely help for carrying this Research work.

Set Point

PCO2

mmHg

Time (sec) Controller Characteristics

min max

Over

Shoot

(%)

Rise Time

(Sec)

Settling

Time

(Sec)

40.9 0 15 2.9 3.8 4.2

43.2 15 30 0.2 15.2 15.6

40.9 30 45 0.08 30.2 30.6

42.62 45 60 0.04 45.1 45.3

41.47 60 75 0.01 60.07 60.1

41.36 75 90 0.08 75.02 75.07

41.57 90 105 0.03 90.00 90.02

42.05 105 120 0.001 105.0 105.05

41.74 120 135 0 120.0 120

41.72 135 150 0 135 135


85

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[2] B. J. E. Misgeld J. Werner M. Hexamer “Robust and self-tuning

blood flow control during extracorporeal circulation in the

presence of system parameter uncertainties”Medical & Biological

Engineering & Computing 2005, Vol. 43

[3] M.Dhinakaran, S.Abraham Lincon, “A Study and Development of

Auto Tuning Control in a Perfusion System for Extracorporeal

Membrane Oxygenator” International Journal of Computer

Applications (0975 – 8887) Volume 106 – No. 16, November 2014

[4] M.Dhinakaran, S.Abraham Lincon, “Perfusion System Controller

Strategies during an ECMO Support” International Journal on

Soft Computing (IJSC) Vol. 5, No. 3, November 2014

[5] M.Dhinakaran, S.Abraham Lincon,P.Praveen Kumar “Modeling and

Controller Design of Perfusion System for Heart Lung Machine”

International Conference on Current Trends in Engineering and

Technology, ICCTET’14 IEEE 2014IEEE - 33344July 8, 2014,

Coimbatore, India,

[6] Hexamer M.,& Werner(2003). “A Mathematical model for the gas

transfer in an oxygenator” IAFC conference on modeling and

control in biomedical systems (pp.409-414).Australia: Melbourne.

[7] Cardiopulmonary Anatomy & PhysiologyEssentials for

Respiratory Care, 4th EditionTerry DesJardins COPYRIGHT ©

2002 by Delmar, a division of ThomsonLearning

[8] Introduction to Extracorporeal Circulation Maria Helena L.

Souza &Decio O. Elias

[9] Scott I. Mars’, Robert H. Bartlett’, Janice M. Jenkins3, & Pierre

Ababa. Controller design for extracorporeal life support (1996)

18th Annual International Conference of the IEEE Engineering in

Medicine and Biology Society, Amsterdam 1996 .1733-1735.

[10] Hammon J Wi . Extracorporeal Circulation: Perfusion System

Cohn Lh, ed. Cardiac Surgery in the Adult. New York: McGraw-Hill,

2008:350-370.

[11] Aidan O'Dwyer “Handbook of PI and PID Controller Tuning

Rules” 2nd Edition Published by Imperial College Press

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Design Methods” Springer-Verlag London Limited 2005


86

A TECHNIQUE TO IMPLEMENT BIMODAL BIOMETRICS FOR

PREVENTING INFANT MIX-UPS USING RASPBERRY PI

S.Sivaranjani1*

, Dr. S.Sumathi 2

1 M.E Scholar, Embedded System Technologies, Sri Sai Ram Engineering College, Chennai, India.

2 Associate Professor, ECE, Sri Sai Ram Engineering College, Chennai, India.

ABSTRACT In developing countries the problem of swapping and abduction of new born is a challenging issue and occurs all over the

world. Traditional methods do not provide required level of security for the newborn. Hence, a newborn personal authentication system is been proposed for this issue based on multi biometrics. In this paper, a bimodal biometric system is been proposed to authenticate the newborn with their mother, based on footprint of newborn and finger print of their mother. This concept is further been enhanced by developing a prototype to be implemented on a Raspberry Pi a single board computer. The Raspberry Pi has System on chip (SoC) denoted as Broadcom BCM2835 SoC, ARM1176JZFS application processor. It increases performance, consumes less power, reduces overall system cost and size. The Raspberry Pi is been controlled by a modified version of Debian Linux optimized for ARM architecture. The required algorithm for processing images for biometric authentication is been performed using open source OpenCV library in Linux platform. Thereby the proposed system improves the security system in

hospitals / birth centers and also provides a low cost solution to the newborn Mix-Ups rather than the expensive DNA procedures. In this paper, discusses about the research works carried on hardware as a biometric module to enhance the performance of a standalone device.

Keywords: OpenCV, Footprint, Fingerprint, Raspberry PI, ARM1176JZFS;

I. INTRODUCTION

Biometric system is a pattern-recognition system

recognizes a person based on feature vector derived

from a specific biological characteristics such as

Physiological biometric identifiers include fingerprints,

hand geometry, ear patterns, eye patterns (iris and

retina), facial features, and other physical

characteristics. Behavioral identifiers include voice,

signature, key stroke, and others.

The Present method of footprint, fingerprint acquisition

in hospitals is inked footprint of the newborn along with

the fingerprint of the mother. This is stored in a file

which forms the medical database. This method of

image acquisition is offline. Another method of practice

is to tie a number band around the hands/legs of the

newborn as a measure of identity. This number band is

same as the one which is also tied to the mother of the

infant. At the time child kidnapping or abduction,

mixing of babies, multiple claims for an infant in any hospitals, birthing centers causes emotional breakdown

and confusion. This raises a question on the

effectiveness of the offline method and the method of

tying number bands (ID bands). This eventually leads to

the DNA test at times. Hence, biometrics can be used to

solve such identity issues.

In the online system, by a digital source and computers

are used for processing and storage. The newborn’s footprint images captured using a high resolution

camera, whose type is Canon EOS 7D. The fingerprint

of the newborn’s mother is also collected by means of a

fingerprint scanner. Bimodal authentication provides

better security when compared with unimodal

authentication. Further, implementation of bimodal

authentication in hardware as embedded system

enhances the overall performance of the system as a

standalone device.

A complex IC that integrates the major functional

elements such as programmable processor, on-chip

memory, accelerating function hardware eg: GPU, both

hardware and software, analog components into a

single chip or chipset is called system on chip (SoC).

Thus, reduce overall system cost, increase

performance, lower power consumption and reduce

size. Raspberry Pi has (SoC) denoted as Broadcom

BCM2835 SoC multimedia processor. The chip used is

ARM1176JZF-S 700MHz, RISC architecture and draws low power. The Raspberry Pi is to build a low-

cost standalone device. It works in Linux platform, In

this paper, discusses about the implementation of

bimodal biometric (fingerprint and footprint) in

Raspberry Pi. Debian Linux platform optimized for

ARM architecture is been used. An open source

computer vision OpenCV is used for biometric


87

programming. It has C++, C, Python and Java

interfaces and supports Windows, Linux, Mac OS, iOS

and Android. It takes the advantage of the hardware

acceleration of the heterogeneous compute Platform.

The OpenCV library is installed from repository by

following few commands to execute the installation of

dependencies, downloading and decompression of

OpenCV, compilation and installation of OpenCV in

Debian Linux. The programming is written in text file

with .cpp extension, the text file is added as executable

files in CMake file and executed using GCC compiler.

Appropriate algorithms are used for reliable

recognition process of Newborn is employed.

II. RELATED WORKS

Some related Research studies have been done on the

field of implementing biometrics into hardware and

Biometric recognition studies related to Infant Mix-

Ups.

S.Balameenakshi, S.Sumathi describes about Biometric

recognition of Newborns Identification using

Footprints concept using Matlab and Labview

software. It uses match score level fusion [7].

Mariano Fons, et.al. Describes about design of

embedded fingerprint matcher system also about the

hardware software co-design of a computational platform responsible for matching two fingerprint

minutiae sets. A novel system concept is suggested by

making use of reconfigurable architectures (FPGA) [8].

Jang-HeeYoo et.al. Describes the design of embedded

biometric systems that identify person by using face-

fingerprint or iris-fingerprint multimodal biometrics

technology and to implement real-time system, the

most time consuming biometric algorithms are implemented in a field programmable gate array. The

hardware platform to demonstrate biometric algorithms

are consisted of a low-power ARM920T core processor

(400MHz). The biometric algorithms have been

effectively optimized for embedded systems and the

results show the possibility of the real-time application

of stand-alone and mobile biometrics [16]. The digital

image processing algorithms in embedded systems

increasingly employ DSP chip or FPGA modules. The

whole system is verified using ARM GNU C under the

embedded Linux operating system.

Sung Bum Pan et al. describes about a VLSI

implementation of minutiae extraction for secure

fingerprint authentication. It presents a system on chip

implementation of the fingerprint feature extraction

algorithm. It is been developed for SoC targeted for

ARM CPU and AMBA bus can also be extended for

many other smart card configurations[17].

Maritane Barrenechea et al. describes about a low-cost

fingerprint minutiae extraction and matching system

based on a Spartan3 family FPGA with embedded

Leon2 open core processor. The architecture

incorporates a floating point unit and a discrete Fourier

transform coprocessor to accelerate the minutiae

extraction process [18].

Stevan o. n. silva, lucianosilva discuss about A

reduced structure of system was created following

the standards of the FHS, containing the necessary

system utilities, supporting file handling, device

configuration, network settings and support for runtime configuration. The structure still takes the

required libraries for applications that use OpenCV.

Startup scripts were rewritten to set up an

environment for implementation of OpenCV and to

start necessary services. Thus, the development of

an embedded architecture based on the Linux

system and the OpenCV library, focused on the

effective use of the hardware, through the selection

of specific features required by this library and aims

for performance improvement[1].

Priyanga .M, Raja ramanan.V discuss about

Unmanned Aerial Vehicle for Video Surveillance

Using Raspberry Pi. Wherein it describes about

PuTTY software is a free and open-source terminal

emulator, serial console .PuTTY's Key Generator is

broken into three main functions: generating,

importing, and exporting keys [3]. PuTTY

generator known as PuTTYGen will automatically

generates a key and provides the key pair for future file transfer.

R N daschoudhary, rajashreetripathy discusses

about real time Face detection and tracking the head

poses position’s from high definition video using

Haar Classifier through Raspberry Pi BCM2835

CPU processor which is an combination of SoC

with GPU based Architecture. It provides only 4.5

frames per second on 4-core CPU it becomes too slow to process HD stream in real time [9].

Therefore, a solution to this problem is been

discussed by parallel modification of OpenCV

algorithm for GPU.

Patricia Melin, Diana Bravo, and Oscar Castillo

describes about pattern recognition using modular

neural networks with a fuzzy logic method for

response integration. It proposes a new architecture for modular neural networks for achieving pattern

recognition in the particular case of human

fingerprints. Response integration is based on the

fuzzy Sugeno integral to combine the outputs of all

the modules in the modular network [13].


88

Ajay Kumar, Sumit Shekhar describes about the

Personal recognition using rank level combination

of multiple palm print representations. It describes

about nonlinear rank level fusion which is helpful

in combining multibiometrics biometric fusion [14].

Pankaj Bhowmik et al. Discusses about Fingerprint

Image Enhancement and its Feature Extraction for

fingerprint recognition in OpenCV (visual c++) for

extracting the minutiae points where a curve track

finishes, intersect with other track or branches off.

A critical step in studying the statistics of

fingerprint minutiae is to reliably extract minutiae

from the fingerprint images [4]. The paper discuss

about image enhancement techniques prior to minutiae extraction to obtain a more reliable

estimation of minutiae locations.

Md Kawser Jahan Raihan et al. describes about

Raspberry Pi image processing based economical

automated toll system. In developing countries

RFID for each car does not exist. RFID is still a

costly solution. Hence, a image processing

technique to detect license plate for auto toll system. The raspberry pi is been used a

minicomputer has the ability of image processing

and control a complete toll system [15]. Thus, it

implies that Raspberry pi provides low-cost

solution for image processing computations.

G.Senthil Kumar et al. describes about embedded

image capturing system using raspberry pi system. A system is been implemented by considering the

requirements of image capturing using Raspberry

pi. Experimental results have shown that designed

system is fast enough to run the image capturing,

recognition algorithm and data stream can flow

smoothly between the camera and Raspberry Pi

board [19].

J. Fierrez-Aguilar, L. -M. Munoz-Serrano, F Alonso-Fernandez and J. Ortega-Garcia discuss

about the effects of image quality degradation on

the verification performance of automatic

fingerprint recognition is investigated. It studies the

performance of two fingerprint matchers based on

minutiae and ridge information under varying

fingerprint image quality. The ridge-based system is

found to be more robust to image quality

degradation than the minutiae-based system for a

number of different image quality criteria is been

concluded [11].

Kazuki Nakajima describes about Footprint-Based

Personal Recognition wherein an input pair of raw

footprints is normalized, both in direction and in

position for robustness image-matching between the

input pair of footprints and the pair of registered

footprints[12]. In addition to the Euclidean distance

between them, the geometric information of the

input foot print is used prior to the normalization,

i.e., directional and positional information. In the

experiment, the pressure distribution of the footprint was measured with a pressure-sensing

mat.

Shepard et.al. (1966) collected footprint of 51

newborns analyzed the sample and were only able

to identify babies, resulting in approximately 20%

identifiable footprints. However it was felt that the

majority of these 20% correctly matched prints

would not stand up under legal scrutiny in courts.

Singh et.al. Describes minutiae-based fingerprint

minutiae matching algorithm to solve two

problems correspondence and similarity

computation. For the correspondence problem,

assigns each minutia two descriptors texture-based

and minutiae-based descriptors and use an

alignment-based greedy matching algorithm to establish the correspondences between minutiae,

extracts a 17-D feature vector from the matching

score using support vector classifier. The

algorithm is tested on FVC2002 databases.

Ajay Kumar, Sumit Shekhar describes about the

Personal recognition using rank level combination

of multiple palm print representations. It describes

about nonlinear rank level fusion which is helpful in combining multi biometrics biometric fusion

[14].

Koichi Ito et al. proposed an efficient fingerprint

recognition algorithm using the phase-based image

matching. The proposed technique is particularly

effective for verifying low-quality fingerprint

images.

III. PROPOSED WORK

The required database is been collected for

authentication consists of 6 samples of same newborn

footprint and corresponding 6 samples of their mothers

fingerprint. Then the collected samples undergoes 5

main steps namely (1) Image Acquisition (2) Image

enhancement (3) Binarization (4) Thinning (5) Feature

* [email protected]


89

Extraction. The extracted features in the form of

template are stored in database known as enrollment

stage. When the input image is given to the proposed

system it undergoes all the preprocessing stage

followed by pattern matching stage finally a decision is

been made based on match score if the given input is

authenticated or not. Implementation of mothers fingerprint recognition in Raspberry Pi is been

employed. All the data required for such recognition is

been stored in SD card of 16 GB (varies depending

upon application) memory. Initially the hardware is

been booted in Linux platform via SD card ideally pre-

installed with NOOBS (New Out Of the Box Software).

Linux terminal can be used to write the command to

download and install open source tool for image

processing called OpenCV library via Keyboard

connected to USB port of Raspberry Pi. OpenCV is a

set of libraries targeted for real time computer vision applications. The programming related to biometrics is

processed on ARM Processor. The result of the

processing after decision level is been displayed on

monitor via HDMI port of Raspberry Pi. Thus, this

hardware helps in developing a chip by verifying and

implementing it using Raspberry Pi and helps in

implementing it as an Application specific integrated

circuits (ASIC).

Advances in Computer Vision have introduced a set of

useful features namely localization, pattern recognition,

counting of objects and people, face recognition tracking

also in the field of Biometrics. The OpenCV library

allows these features to be implemented on computers

with relative ease, but general purpose hardware prevents

a large number of applications, makes it too expensive,

with large physical size, high power consumption and low

tolerance to vibration and changes in environmental

temperature [1]. OpenCV program, which is integrated

with Raspberry Pi to build a low-cost stand-alone device is been employed. The Raspberry Pi is controlled by a

modified version of Debian Linux optimized for ARM

architecture. The core frequency is been set to 700MHz.

GPU is capable of Blu-ray quality playback, using H.264

at 40Mbits/s. It has GPIO where its header consists of 17

pins [3]. The front End Graphical interface can be used for

developing application in C++ and cross compiled for

ARM architecture.

Stressing this hardware by selecting specific features of

the system that provide support to applications based on

this library in order to minimize the processing overhead

and reduce the use of computing resources such as main

and storage memories [1] is been employed. Raspberry Pi

board depicted in fig 1.

Fig 1: The Raspberry Pi board

Source: www.raspberrypi.org

3.1. OPENCV LIBRARY

The OpenCV (Open Source Computer Vision Library) is

a Computer Vision library written in C and C++ compatible with Linux. It has been designed to be

computationally efficient, with a strong focus on real-

time applications and additionally it takes the advantages

of CPUs with multiple cores [Bradski, &Kaehler, 2008].

The purpose of this library is that it provides a simple

computer vision infrastructure to prototype quickly for

sophisticated applications. It has over 2500 optimized

algorithms, includes both a set of classical algorithms and

the state of the art algorithms in Computer Vision, which

is been used in image processing, detection and face

recognition, object identification, classification actions, traces, and other functions. The library is used

extensively by companies like Google, Yahoo, Microsoft,

Intel, IBM, Sony, Honda, Toyota, and startups area as

Applied Minds, Video Surf and Zeitera, and research

groups and government .OpenCV provides better

portability [1].

3.2 EMBEDDED VISION: Fingerprint recognition in

Raspberry Pi

The boot of raspberry pi is done via SD card ideally

preinstalled with NOOBS and the option of Debian Linux

is been chosen during boot process. Few commands have been used in Linux terminal to install OpenCV, GCC,

Numpy, Scipy, Matplotlib, Cmake, files necessary for

developing biometrics Programming on Debian Linux

Platform.

$ Sudo apt-get update

$ Sudo apt-get upgrade

$ Sudo apt-get install build-essential $Wget fossies.org/linux/misc/opencv-2.4.9.zip


90

$Sudo apt-get installlibav format-devlibgtk 2.0-dev pkg-

configCmake-libswscale-dev bzip2

$unzip opencv-2.4.9

$cd opencv-2.4.9.zip

$mkdir build

$cd build

$Cmake –D CMAKE_BUILD_TYPE=BUILD –DCMAKE_INSTALL-PREFIX=/URS/LOCAL-D

$sudoviopencv.conf I <

/etc/ld.so.conf.d/ /usr/local/lib Esc: wq

$Make

$Sudo make install

$sudoldconfig

$sudoviopencv.conf /etc/bash.bashrc.

$apt-get install matplotlib

All the libraries are downloaded via Ethernet port of

raspberry Pi connected to internet. Results of biometric

implementation of both using OpenCV with .cpp text file

and with python shell by importing OpenCV library is

been implemented. In case of executing it in python shell

additional dependencies are required to be installed such

as matplotlib, scipy, math, numpy. Tkinter tool which is inbuilt in python is been used for creating GUI.

Reliability of fingerprint recognition depends on the

performance of minutiae extraction algorithm relies on

the input quality of images. Thus, enhancement of image

is processed by Histogram equalization, this method

usually increases the global contrast of the image, and the

intensities can be better distributed on the histogram by effectively spreading out the most frequent intensity

values.

Fig 2: Depicts the Histrogram Equalization plot of a

finger print in Raspberry pi. Fig 3: shows the enhanced

and thinning of a fingerprint using OpenCV in Pi board

where threshold is set to 127/255. Fig 5 shows the

Adaptive equalized image of a fingerprint.

Fig 2: Histogram equalization of finger print.

Fig 3: Enhanced and thinning of a fingerprint using OpenCV.

The analysis of images are further done by extracting the

high frequency components of a fingerprint is been

shown in fig 4.

Fig 4: High frequency component of a fingerprint image.

Fig 5: Adaptive Equalized image of a fingerprint.

After image enhancement process the image undergoes

binarization of an image. Binarization is really an

important step in the process of ridge extraction. The

image is divided into blocks (16X16) matrix and the mean intensity value is calculated, if each pixel values in

the matrix is larger than mean intensity value of current

block then the pixel value is been made high ‘1’

otherwise, considered as low ‘0’. Thinning of an image is

been performed which is total of one pixel width. The

thinning of a fingerprint result using morphological

operation is been shown in fig 6.


91

Fig 6: Binarization and Thinning of a fingerprint.

It is been further analyzed that Guo hall’s parallel

Thinning Algorithm in OpenCV provides better in

detecting the end points in images. Fig 3 shows the

fingerprint thinning using Guo hall’s Algorithm using

OpenCV in c++. Parallel thinning with two sub-iteration

algorithms is described below:[20]

3x3 Template Used For Pixel Removal

p8 p1 p2

p7 P p3

p6 p5 p4

Algorithm: Let C (P) be the number of distinct 8-

connected components of 1's in Ps 8-neighborhood.

N(P) =Min(N1(P), N2(P))

N 1(P) = (p1vp2)+(p3vp4)+(p5vp6)+(p7vp8)

N2 (P) = (p2vp3)+(p4vp5)+(p6vp7)+(p8vp1)

An edge point will be deleted if it satisfies: a) C(P)=l;

b) 2<=N(P)<=3;

c) Apply one of the following:

1) (P2vP3vP5)*P4=0 in odd iterations; or

2) (P6vP7vP8)*P8=0 in even iterations

Where "v" expresses the logic "OR" operation. C(P)=1

Means P is 8-simple.

There is only one group of 8-connected 1's around P. Due

to this condition, deletion of P will not break the

connectivity of the elements in the 3X3 window under processing. Condition (a) guarantees P is not a break

point. The use of N (P) allows one to identify the end

points if or not they have one or two 1's 8-neighbors [20].

To obtain unique features of fingerprint minutiae is been

calculated and stored for template match. After the

process of minutiae extraction false H breaks (False

minutiae) in fingerprint is been removed using Euclidean

distance algorithm. Fig 7 shows the minutiae extraction

of a fingerprint image using python-OpenCV interface.

Fig 7: Minutiae Extraction of a fingerprint.

Minutiae extraction works after image thinning process.

Different types of minutiae are ridge ending and

bifurcation. These minutiae are detected using the

concept of Crossing Number (Cn) based on Rutovitz’s

definition of crossing number for a pixel P is given

below[21]:

Where Pi is the binary pixel value in the neighborhood of

P with Pi = (0 or 1). The crossing number Cn (P) at a

point P is defined as half of cumulative successive differences between pairs of adjacent pixels belonging to

the 8- neighborhoods of P. If Cn(P) = = 1 it’s a ridge

ending and if Cn(P) = = 3 it’s a ridge bifurcation point. A

3X3 matrix slide over the thinned fingerprint image to

detect candidate minutiae. For example, if the matrix

matches the pattern shown in fig 8, it can be concluded

that whether P is a ridge ending or bifurcation.

Fig 8: Two sample 3X3 matrix pattern of ridge ending and bifurcation.

The ridge endings are marked in blue color and

bifurcation with red color in programming result of

which is shown in figure 9. After detecting all the

possible minutiae, the next step is to validate all minutiae to eliminate false minutiae before matching stage.

Fig 9 also depicts the filtered minutiae after the removal

of false minutia structures (e.g. spikes, bridges, holes,

breaks, spurs, ladder structures) by keeping minimum

Euclidean distance equal to 10.0.


92

Fig 9: Shows the Minutiae detection and filtered minutiae of a fingerprint in Raspberry Pi.

Biometric systems can be configured to make a match or

no match decision, based on a predefined number,

referred to as a threshold, which establishes the

acceptable degree of similarity between the trial template

and the enrolled reference template. After the

comparison, a score representing the degree of similarity

is generated and this score is compared to the threshold to make a match or no-match decision.

Similarly, the work on foot print images has to be

employed and feature vector has to be calculated and

appropriate fusion schemes as to be employed. The six

samples of images has been collected out of which 3 are

trained images and remaining 3 are considered to be

testing images, based on which a statistics as to be evaluated based on False Acceptance Rate (FAR) and

False Rejection Rate (FRR). Such that genuine

acceptance rate can be calculated which is equal to 1-

FAR.

IV. CONCLUSION

This paper discusses the implementation of low cost ambient new born authentication System based on

biometric traits of the Infant mothers fingerprint. On

implementing this concept on Raspberry Pi further

enhances the performance. The biometric processing is

been employed by installing open source OpenCV library

into a Linux platform. Appropriate algorithm for

fingerprint enhancement, feature extraction is been

employed in Raspberry pi. Hence this method is a low

cost solution to the newborn swapping rather than the

expensive DNA procedure. A review on different

hardware implementation as a biometric module also been discussed.

References

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[2] Ricardo Neves, Anibal C. Matos “Raspberry PI Based Stereo

Vision For Small Size ASVs” INESC TEC, 2014.

[3] Priyanga .M, Raja ramanan . V “ Unmanned Aerial Vehicle for video surveillance Using Raspberry Pi” International journal of

Innovative Research in Science, Enginerring and Technology,

March 2014.

[4] Pankaj Bhowmik, Kishore Bhowmik, Mohammad NurulAzam,

Mohammed WahiduzzamanRony “ Fingerprint Image Enhancement And its feature Extraction for recognition”,

International Journal of Science and Technology Research Volume 1,Issue 5, June 2012.

[5] Anil jain, Karthik Nandakumar and Arun Ross,”score

Normalisation in multimodal biometric systems”, Pattern Recognition 38(2005)2270 2285.

[6] S. Zhang, R janaKiraman, T. Sim and S. Kumar, “Continuous

Verification Using Multimodal Biometric System” Proc. Second Int’l conf. Biometrics, pp.562-570,2006.

[7] S.Balameenakshi. S.Sumathi, “Biometric Recognition of

Newborns Identification using Footprints”, Proceedings of 2013 IEEE International Conference on Information and

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[8] Mariano Fons, Francisco Fons, Enrique Cantó,” Design of an Embedded Fingerprint Matcher System”, 2006 IEEE.

[9] R.N daschoudhary, rajashree tripathy, “Real time Face Detection

and tracking using haar classifier on SOC, SARC-IRF International conference ,12th April 2014, New Delhi,India.

[10] Koichi Ito, Ayumi Morita, TakafumiAok, Tatsuo Higuchi Hiroshi Nakajima$, and Koji Kobayashi “A fingerprint Recognition

Algorithm using Phase-Based Image Matching for Low-Quality Fingerprints”,2005,IEEE.

[11] J. Fierrez-Aguilar, L. -M. Munoz-Serrano, F Alonso-Fernandez

and J. Ortega-Garcia, ”On the effects of image Quality Degradation on minutiae and ridge-based automatic fingerprint

recognition”,2005.

[12] Kazuki Nakajima ”Footprint –Based Personal Recognition “,IEEE Transactions on Biomedical Engineering, vol

47.No.11,November 2000.

[13] Patricia Melin, Diana Bravo, and Oscar Castillo “Fingerprint Recognition using modular neural networks and fuzzy integrals

for response Integration”, International joint conference on neural networks, Montreal,Canada,july 31- august 4 2005.

[14] Ajay Kumar, Sumit Shekhar “ Palmprint Recognition using Rank

Level fusion”, 2010 IEEE 17th International Conference on image processing , September 26-29,2010,Hong Kong.

[15] Md. Kawser Jahan Raihan, Mohammad Saifur Rahaman,

Mohammad Kaium Sarkar and Sekh Mahfuz “Raspberry Pi Image processing Based Economical Automated Toll system” Global

journal of researches in engineering electrical and electronics Engineering. Vol 13 issue 13 version 1.0 year 2013.

[16] Jang-Hee Yoo, jong-Gook Ko, Yun-Su chung, Sung-Uk Jung, Ki-Hyun Kim, Ki-Young Moon anf Kyoil Chung. ETRI-information

security research division. “ Design of embedded multimodal Biometric systems” IEEE.

[17] Sung Bum Pan, Darsung Moon, KichulKim, Yongwha Chung “

A VLSI implementation of minutiae extraction for secure fingerprint authentication” IEEE 2006.

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Cost FPGA-based embedded fingerprint verification and matching system” signal theory and communications group, department of

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93


93

FEATURE EXTRACTION AND COMPARISION OF MOTOR ACTIVITIES

FOR CURSOR CONTROL

Sasweta Pattnaik1, Manasa Dash

2, Sukant Sabut

1*

1Department of Electronics & Instrumentation Engineering

Institute of Technical Education & Research

SOA University, Bhubaneswar, Odisha

2Department of Mathematics

Silicon Institute of Technology

Bhubaneswar, Odisha *Corresponding author

ABSTRACT

Brain-computer interface (BCI) is a hardware and software communication system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis(ALS), brain stem

stroke, or spinal cord injury. The use of Electroencephalogram (EEG) signals in the field of BCI has obtained a lot of interest with diverse applications ranging from medicine to entertainment. One major challenge in current BCI research is how to extract features of time-varying EEG signals and classify the signals as accurately as possible. In this paper, we have extracted the features of EEG signal obtained from the database of BCI project by discrete wavelet transform (DWT) for backward imagery and backward movement with task of left and right hand. The results were compared for mean, standard deviation and peak power values.

Keywords: Brain Computer Interface (BCI); Electroencephalograph (EEG); Wavelet Transform, Movement with task,

Imagery Movement

I. INTRODUCTION

In BCI (Brain Computer Interface), people can send

information simply by thinking. This is used to enable

communication for severely disabled user. All interfaces

(such as keyboards, mice, or voice recognition systems)

require motor movement. So, BCIs are designed to enable

communication for severely disabled people like Spinal

Cord Injuries.

The electrophysiological phenomena

investigated most in the quest for an automatic

discrimination of mental states are event-related potential

(ERP) [1], and localized changes in spectral power of

spontaneous EEG related to sensorimotor processes [2, 3].

Beta is the brain wave usually associated with active

thinking, active attention, and focus on the outside world or solving concrete problems. The original EEG signal is time

domain signal and the signal energy distribution is

scattered. The signal features are buried away in the noise.

In order to extract the features, the EEG signal is analyzed

to give a description of the signal energy as a function of

time or/and frequency. Based on previous studies, features

extracted in frequency domain are one of the best to

recognize the mental tasks based on EEG signals [4]. The

first analysis method was the Fast Fourier Transform (FFT)

by applying the discrete FFT to the signal and finds its

spectrum. As EEG signals are non-stationary, traditional

feature extraction method such as amplitude-frequency analysis based on FFT only uses amplitude and frequency

information and not time domain information. However,

much research has shown that the information of time

domain is very important for improving the classification performance of EEG signals. Wavelet decomposition is a

good time-frequency analysis method since wavelets allow

decomposition into frequency components while keeping as

much time information as possible. Event-related

Desynchronization (ERD) and Event-related

Synchronization (ERS) are the underlying

neurophysiological phenomena accompanying motor

imagery. It provides the theoretical basis of the motor

imagery-based BCI research [5-7]. The rhythm µ (8-12 Hz)

and β (18-26 Hz) originating in the sensorimotor cortex

have been postulated to be good signal features for EEG-

based BCI [6, 7]. Ales et al. [8] used both DFT and DWT method for feature extraction, classified the features

extracted from both the methods by neural network

classifier and compared them. They found DWT is proved

the efficient method as compared to DFT for feature

extraction. Hence the wavelet based feature extraction is

considered as the best when compared with any other

transform based feature extraction. Various frequency

domain feature extraction methods show that the wavelet

decomposition method is more powerful for extraction than

of DFT and STFT because of its multi-resolution

characteristics [9]. In wavelet transform we get the localized information for low frequencies (high time

resolution) and in the transient events (high frequency

resolution). Homri et al. applied the Daubechies, Coiflet

and Symmlet wavelet families to a dataset of MI to extract

features and describe right and left hand movement imagery


94

[10]. The current work is the extraction of features from

EEG recorded signal and its statistical analysis for the

classification of signal for BCI application.

II. DESCRIPTION OF DATASET

The first step in BCI systems is the data collection and

filtering. The datasets used are the dataset 2-2D motion of

EEG motor activity dataset of the BCI project. The data are

recorded from a 21 year old, right handed male with no known medical conditions. The EEG consists of actual

random movements of left and right hand recorded with

eyes closed. Each row represents one electrode.The order of

the electrodes is FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8

T3 T4 T5 T6 FZ CZ PZ. The recording was done at 500Hz

using Neurofax EEG System which uses a daisy chain

montage. Two experiments were carried out by taking a

single channel (C3) EEG. First One is the comparison

between left hand backward imagery movement and

backward movement with task. And second one is the

comparison between right hand backward imagery movement and movement with task.

III. METHODS:FEATURE EXTRACTION BY DISCRETE

WAVELET TRANSFORM (DWT)

The key feature of wavelets is the time-frequency

localization. It means that most of the energy of the wavelet

is restricted to a finite time interval. When compared to

STFT, the advantage of time-frequency localization is that

wavelet analysis varies the time-frequency aspect ratio,

producing good frequency localization at low frequencies

(long time windows), and good time localization at high

frequencies (short time windows). This produces a

segmentation, or tiling of the time-frequency plane that is

appropriate for most physical signals, especially those of a transient nature. The wavelet technique applied to the EEG

signal will reveal features related to the transient nature of

the signal, which are not obvious by the Fourier transform.

In numerical analysis and functional analysis, a discrete

wavelet transform (DWT) is any wavelet transform for

which the wavelets are discretely sampled. In general, it

must be said that no time-frequency regions but rather time-

scale regions are defined. The DWT of a signal is

calculated by passing it through a series of filters. First the

samples are passed through a low pass filter with impulse

response g resulting in a convolution of the two. The signal is also decomposed simultaneously using a high-pass filter

h. The output provides the detail coefficients (from the

high-pass filter) and approximation coefficients (from the

low-pass). It is important that the two filters are related to

each other and they are known as a quadrature mirror filter.

All wavelet transforms can be specified in terms of a low-

pass filter g, which satisfies the standard quadrature mirror

filter condition,

G(Z)G(Z-1)+G(-Z)G(-Z-1)=1 ………………(1)

where G(z) denotes the z-transform of the filter g. Its

complementary high-pass filter can be defined as,

H(Z)=ZG(-Z-1) ………………(2)

DWT employs two sets of function, scaling functions and

wavelet functions, which are related to low-pass and high-

pass filters, respectively. The decomposition of the signal

into the different frequency bands is merely obtained by

consecutive high-pass and low-pass filtering of the time

domain signal. The procedure of multi-resolution

decomposition of a signal x[n] is schematically shown in Figure1. Each stage of this scheme consists of two digital

filters and two down-samplers by 2. The first filter, h [.] is

the discrete mother wavelet, high-pass in nature, and the

second, g[.] is its mirror version, low-pass in nature. The

down-sampled outputs of first high-pass and low-pass

filters provide the detail, D1 and the approximation, A1,

respectively. The first approximation, A1 is further

decomposed and this process is continued [11].

Selection of suitable wavelet and the number of

decomposition levels is very important in analysis of

signals using the DWT. The number of decomposition

levels is chosen based on the dominant frequency

components of the signal. The levels are chosen such that

those parts of the signal that correlates well with the

frequencies necessary for classification of the signal are

retained in the wavelet coefficients.

Fig.1. Sub-band decomposition of DWT implementation;

h[n] is the high-pass filter, g[n] the low-pass filter

IV. RESULTS AND DISCUSSION

The recorded single channel EEG (C3 channel) signal is

taken from database, the 50Hz transmission line frequency

is removed by notch filter, bandpass filtered between 0.5 to

30 Hz, and the result is represented in figure 2 and 3. The

pre-processed signal is decomposed using discrete wavelet

transform (db4) which is shown in figure 4 and 5. Table 1

presents the frequencies of different levels of wavelet

decomposition on the EEG signals with a 500 Hz sampling rate. The µ rhythm (8-12 Hz) and β rhythm (18-26 Hz)

* Corresponding author e-mail address

http://en.wikipedia.org/wiki/Low_pass_filter

http://en.wikipedia.org/wiki/Impulse_response

http://en.wikipedia.org/wiki/Impulse_response

http://en.wikipedia.org/wiki/Convolution

http://en.wikipedia.org/wiki/High-pass_filter

http://en.wikipedia.org/wiki/Quadrature_mirror_filter


95

originating in the sensorimotor cortex were considered to be

good signal features for EEG-based BCIs. The Mean and

standard deviation are calculated and results are compared

for leftbackward imagery Vs leftbackward movement with

task. The result also compared for rightbackward imagery

Vs right backward movement with task, which is given in

table 2, 3 and the peak power values are given in table 4, 5

for backward imagery and backward movement with task

for left and right hand respectively. These feature vectors,

calculated for the sub-bands D5 and D4 reflecting the µ and

β rhythms, respectively, were used for the classification of EEG signals. The tests are done with different types of

wavelets, and the one that gave maximal efficiency was

selected for the particular application.

We have taken Daubechies wavelet of order4 [12].The

level of decomposition is chosen upto 6 for 500Hz

sampling frequency for backward imagery and backward

movement with task. Table 2 shows further reduction of the

feature vector of extracted features. Mean represents Frequency distribution of signal and standard deviation

represents the amount of variability in frequency

distribution. It is found out that the mean ,standard deviation

and peak power of backward movement with task EEG is

high than that of EEG of backward imagery movement in

D4 level (β band).

(a)

(b)

Fig.2. (a) EEG of leftbackward imagery (b) Leftbackward movement with task

(a)

(b)

Fig.3. (a) Rightbackward imagery and (b)Rightbackward movement with task

Fig.4.Wavelet decomposition of leftbackward imagery

Movement

0 1000 2000 3000 4000 5000 6000 7000 8000-50

0

50

100

time in ms

Am

plit

ude

raw EEG

0 1000 2000 3000 4000 5000 6000 7000 8000-2

0

2

4

time in ms

Am

plit

ude

Bandpass filtered EEG

0 500 1000 1500 2000 2500 3000 35000

50

100

time in ms

Am

plit

ude

raw EEG

0 500 1000 1500 2000 2500 3000 3500-4

-2

0

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time in ms

Am

plit

ude


0 1000 2000 3000 4000 5000 6000 7000 80000

50

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time in ms

Am

plit

ude

raw EEG

0 1000 2000 3000 4000 5000 6000 7000 8000-4

-2

0

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time in ms

Am

plit

ude


0 500 1000 1500 2000 2500 3000 3500-50

0

50

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time in ms

Am

plit

ude

raw EEG

0 500 1000 1500 2000 2500 3000 3500-2

-1

0

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time in ms

Am

plit

ude


0 1000 2000 3000 4000 5000 6000 7000 8000-5

0

5Bandpass filtered eeg signal

0 1000 2000 3000 4000 5000 6000 7000 8000-1

0

1x 10

-3 Detail D1

0 1000 2000 3000 4000 5000 6000 7000 8000-0.05

0

0.05Detail D2

0 1000 2000 3000 4000 5000 6000 7000 8000-0.1

0

0.1Detail D3

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0

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0

2Detail D5

0 1000 2000 3000 4000 5000 6000 7000 8000-1

0

1Detail D6

0 1000 2000 3000 4000 5000 6000 7000 8000-0.5

0

0.5Approximation A6


96

Fig.5.Wavelet decomposition of leftbackward movement with task

TABLE.1. Frequencies corresponding to different levels of

decomposition for daubechies wavelet of order 4 with a sampling

rate 500HZ

TABLE.2.Leftbackward imagery Vs Leftbackward

Movement with task using mean and standard deviation

TABLE.3. Rightbackward imagery Vs Rightbackward Movement

with task using mean and standard deviation

TABLE.4. Leftbackward imagery Vs Leftbackward

Movement with task using Peak Power

Leftbackward

imagery

Leftbackward

Movement

Peak power

in D5(µv2)

67 11

Peak power

in D4(µv2)

4.4 4.7

TABLE.5. Rightbackward imagery Vs Rightbackward

Movement with task using Peak Power

Rightbackward imagery

Rightbackward Movement

Peak power in D5(µv2)

62 20.08

Peak power in D4(µv2)

6.37 7.04

V. CONCLUSION

The work has proven that the features based on µ and β

rhythms which are very effective for motor imagery

classification are the reduced feature vector as input to the

classifier. The features are extracted from the EEG data by

DWT. They are good features that can improve the classification of mental tasks. We can say as detection

techniques and experimental designs improve, BCI will

improve as well and provide economic alternatives for

individuals to interact with their environment. EEG based

BCI are safe and low invasive. From the result analysis, we

concluded that the wavelet feature extraction scheme is best

suited for EEG feature extraction in BCI applications.

Further study is to analyze the classification accuracy of the

extracted features.

Acknowledgment

The EEG data were recorded for the initial work in the IMS and SUM Hospital, Bhubaneswar; thanks to Dr. Subhranshu Jena and the EEG record technician for their support to provide the EEG data.

0 500 1000 1500 2000 2500 3000 3500-5

0

5Bandpass filtered eeg signal

0 500 1000 1500 2000 2500 3000 3500-5

0

5x 10

-3 Detail D1

0 500 1000 1500 2000 2500 3000 3500-0.05

0

0.05Detail D2

0 500 1000 1500 2000 2500 3000 3500-0.5

0

0.5Detail D3

0 500 1000 1500 2000 2500 3000 3500-1

0

1Detail D4

0 500 1000 1500 2000 2500 3000 3500-2

0

2Detail D5

0 500 1000 1500 2000 2500 3000 3500-0.2

0

0.2Detail D6

0 500 1000 1500 2000 2500 3000 3500-0.5

0

0.5Approximation A6

Frequency

band(Hz)

Decomposition

levels

EEG Rhythms

125-250 D1 noise

62.5-125 D2 noise

31.25-62.5 D3 noise

15.62-31.25 D4 β

7.81-15.62 D5 µ

3.91-7.81 D6 θ

0-3.9 A6 δ

Leftbackward

imagery

Leftbackward

Movement

Frequency

Bands

Mean ± S.D. Mean ± S.D.

D4 0.1135± 0.1548 0.1619± 0.2174

D5 0.3112± 0.4129 0.2117± 0.2934

D6 0.1220 ± 0.1628 0.0464± 0.0587

A6 0.2239± 0.1079 0.2068± 0.0429

Rightbackward

imagery

Rightbackward

Movement

Frequency

Bands

Mean ± S.D. Mean ± S.D.

D4 0.1298 ± 0.1881 0.2103± 0.3112

D5 0.3573 ± 0.4603 0.2121± 0.2964

D6 0.1386 ± 0.1883 0.0520± 0.0755

A6 0.2858 ± 0.0749 0.0278± 0.0326


97

References

[1] L.A. Farwell, E. Donchin, “Talking off the top of your head toward a

mental prosthesis utilizing event-related brain potentials”,

Electroencephalography and Clinical Neurophysiology, vol. 70(6), pp.

510-523, 1988. [2] G. Pfurtscheller, C. Neuper, D. Flotzinger, M. Pregen-zer, “EEG-based

discrimination between imagination of right and left hand movement”,

Electroencephalography and Clinical, Neurophysiology, vol. 103(6),

pp. 642-651, 1997.

[3] J.R. Wolpaw, D.J. McFarland, “Multichannel EEG-based brain-

computer communication”, Electroencephalography and Clinical

Neurophysiology, vol. 90(6), pp. 444-449, 1994.

[4] G.N.Molina, T.Ebrahimi, J.M. Vesin, “Joint Time-Frequency-Space

Classification of EEG in a Brain-Computer Interface Application”,

EURASIP Journal on Applied Signal Processing ,vol.7,pp.713-729, 2003.

[5] G. Pfurtscheller, “Functional brain imaging based on ERD/ERS”,

Vision Research, vol.41 (10-11). pp.1257-1260, 2001.

[6] G. Pfurtscheller, SFH. Lopes, “Event-related EEG/MEG

synchronization and desynchronization: basic principles”, Clinical

Neurophysiology , vol.110(11), pp.1842-1857, 1999.

[7] G. Pfurtscheller, C. Brunner, A. Schlaogl, SFH. Lopes, “ Mu rhythm

(de)synchronization and EEG single-trial classification of different

motor imagery tasks”, NeuroImage , vol 31(1), pp.153-159, 2006.

[8] A. Prochazka, J. Kukal, O. Vysata, “Wavelet Transform Use for

Feature Extraction and EEG Signal Segments Classification”, Int.

Symp on Communication, Control and Signal Processing,vol.5,pp. 719 - 722, 2008.

[9] C.E. Mohan, S.V. Dharani, “Wavelet based feature extraction scheme

of electroencephalogram”, Int Journal of Innovative Research in

Science, Engineering and Technology, vol.3 (1), pp.1-6, 2014.

[10] I. Homri, S. Yacoub, N. Ellouze, “Optimal segments selection for

EEG classification”, 6th Int Conf on Sciences of Electronics,

Technologies of Information and Telecommunications (SETIT),

Sousse, Tunisia, pp.817-821,2013.

[11] P. Jahankhani, V. Kodogiannis, K. Revett, “EEG Signal

Classification Using Wavelet Feature Extraction and Neural

Networks”, IEEE International Symposium on Modern Computing,

pp. 52-57, 2006.

[12] Y. Fang, X. Zheng, “Feature Extraction of Time-Amplitude-

Frequency Analysis for Classifying Single EEG”, Journal of Fiber

Bioengineering and Informatics, vol.7.2, pp. 261-271, 2014.

2nd

International Conference on Bio Signals, Images and Instrumentation, (ICBSII - 2015).

98

NON-INVASIVE DEVICE FOR MEASUREMENT OF GLUCOSE AND

HAEMOGLOBIN IN BLOOD

K.M.Nivetha1,K.Pavithra

2,N.Indhujha

3,D.Arulkumar

4

1,2,3 UG students, Electronics and Communication Engineering,Panimalar institute of technology(Anna

University),Chennai,Tamilnadu,India

4 Assistant Professor,Electronics and Communication Engineering,Panimalar institute of technology(Anna

University),Chennai,Tamilnadu,India

ABSTRACT

Diabetes has evolved as a major muscle eating disease which leads to the reduction of hormone erythroprotein responsible

for red blood cell count in our body.This reduction of red blood cell leads to anemia,stroke and other major

disease.Therefore maintaining of glucose and haemoglobin level is mandatory to avoid diabetes,other short term and long

term disease.Present available invasive technologies for measuring glucose and haemoglobin involves frequent pricking of

fingers to take the blood sample which increase patient’s discomfort and loss of blood.In our project the proposed non

invasive method uses NIR-occlusion spectroscopy to measure the glucose and haemoglobin level in blood.Therefore this

method reduces patient’s discomfort and compliance.It also supports effective monitoring of blood and glucose and

haemoglobin level in blood.

Keywords: Diabetes,Anemia, NIR-occlusion spectroscopy

INTRODUCTION

Diabetes is now becoming a fastest growing

disease among old aged and middle aged people. As of 2014, an estimated 387 million people have

diabetes worldwide, with type 2 diabetes making

up about 90% of the cases. This is equal to 8.3% of

the adult population, with equal rates in both

women and men.Diabetes, often referred to by

doctors as diabetes mellitus, describes a group of

metabolic diseases in which the person has high

blood glucose (blood sugar), either because insulin

production is inadequate, or because the body's

cells do not respond properly to insulin, or both. Patients with high blood sugar will typically

experience polyuria (frequent urination), they will

become increasingly thirsty (polydipsia) and hungry (polyphagia).

Diabetes mellitus often described by three types

namely:Diabetes-Type 1, type 2and gestational

diabetes.

Type 1 DM results from the body's

failure to produce enough insulin. This form

was previously referred to as "insulin-

dependent diabetes mellitus" (IDDM) or

"juvenile diabetes". The cause is unknown

Type 2 DM begins with insulin resistance,

a condition in which cells fail to respond to

insulin properly.] As the disease progresses a

lack of insulin may also develop. This form

was previously referred to as "non insulin-

dependent diabetes mellitus" (NIDDM) or

"adult-onset diabetes". The primary cause is

excessive body weight and not enough

exercise.

Gestational diabetes, is the third main

form and occurs when pregnant women

without a previous history of diabetes develop

a high blood glucose level.

A normal blood sugar during fasting(i.e.,8 hours

before eating )should be 70-99mg/dl.Normal blood

sugar two hours after eating should be less than

140mg/dl.[5]

About 25 percent of people with diabetes have

some level of anemia. In anemia, there are fewer

red blood cells than normal, resulting in less oxygen being carried to the body’s cells. People with anemia often feel tired or weak and may have

difficulty getting through activities of daily living.

Other symptoms include paleness, poor appetite,

dizziness, lightheadedness, rapid heartbeat, and

shortness of breath. Because these symptoms can

http://en.wikipedia.org/wiki/Diabetes_mellitus_type_1

http://en.wikipedia.org/wiki/Diabetes_mellitus_type_2

http://en.wikipedia.org/wiki/Insulin_resistance

http://en.wikipedia.org/wiki/Diabetes_mellitus#cite_note-WHO2013-3

http://en.wikipedia.org/wiki/Gestational_diabetes

2nd


99

also be associated with diabetes, they are

sometimes not recognized as evidence of

anemia.Anemia may occur with diabetes because

the hormone that regulates red blood cell

production, erythropoietin (EPO), is produced by

the kidneys. Kidney damage at several levels is a complication of diabetes, and one problem often

leads to the other. Changes in the kidneys that

occur with diabetes range from diabetic

nephropathy all the way to chronic kidney disease.

Early detection and treatment is essential to prevent

or delay disease progression. If anemia is present ,

the blood glucose tests may not be accurate.

Studies that looked at blood glucose monitor

accuracy found that low hematocrit levels can

falsely increase glucose measurements, leading to

monitor test results as much as 20 percent too high. All glucose monitors are designed to measure the

level of glucose in the blood, but, unfortunately,

not all blood is the same. A major difference between blood samples is the percentage of red

blood cells, or hematocrit. The average hematocrit

for men is slightly higher than the average for

women. Young children tend to have a lower

hematocrit than adults. As people age, hematocrit

values usually are lower.Low hematocrit is a

common side effect of many illnesses and of drug

therapies like metformin. Reductions in kidney

function that occur in diabetes can also cause lower

hematocrit values.Normal range for haemoglobin:

In man:13.5-18gm/dl

In woman:11.5-16gm/dl

New born:14-24gm/dl

EXISTING METHOD

Currently, blood glucose and haemoglobin can only be monitored through the use of invasive

techniques. The risk of infection and measurement

inaccuracy are present with all of the invasive

techniques. In addition, discomfort also caused by

the invasive method.

Fig1.1:Haemoglobin

Fig1.2:Glucose

AVAILABLE TECHNOLOGIES There are many non invasive techniques like

near-infrared and Raman spectroscopy,

polarimetry, light scattering, photoacoustic

spectroscopy, polarization technique, mid-

infrared spectroscopy etc are available to measure the glucose level in blood.Pulse oximetry technique

is the present available method to measure

haemoglobin.But there is no non invasive device

for both glucose and haemoglobin measurement.

PROPOSED METHOD The proposed method uses the principle of

NIR-occlusion spectroscopy[6].Due to the use of

this ooclusion principle for glucose and

haemoglobin measurement effective results are

obtained .

Fig.2 Basic Block Diagram

2nd


100

The figure shows the block diagram of the

device. It consists of two NIR reflective sensors[8][2]

correspondingly in the range of 940nm and

870nm.Switches are placed before sensors to select

the reqired sensor When the finger is placed on the

sensor unit, the IR rays emitted by the sensor (LED) passes through the finger and the reflected

waves from the tissue are received by the sensor

(photodetector). Photodetector convert the light

signal into the electrical signal which is given to

the low pass filter. Low pass filter filters the noise

from the detector signal and it is then amplified.

This amplified signal is given into the

microcontroller unit. The microcontroller

PIC16F877A contains an inbuilt Analog to Digital

Converter and it converts the analog signal into

digital values. These digital values are displayed in

the seven segment LCD display. For each parts to operate, 5V power supply is provided.

WORKING PRINCIPLE

The sensing unit consists of two sensors

namely 940nm for glucose measurement and

870nm for haemoglobin measurement.during

measurement the user selects the required switch to

drive the corresponding sensor.The rays from the

sensor pass through the finger. The photodetector

detects the emitted ray from the finger. Occlusion

is the technique of using a pneumatic cuff to resist

blood flow for the minimum amount of time

possible.The flow is resisted with an amount of

pressure that doesn’t exceed the systolic pressure. Occlusion process amplifies the signal amplitude,

decrease the motion noise effect and discriminate

overlapping wavelengths. This enhances the

sensitivity to glucose and Haemoglobin.It helps in

producing the accurate result. NIR spectroscopy

involves sending the infrared rays to a greater depth

in to the skin to measure the required cells in the

blood.The penetration depth varies from 1 to

100mm.to have deeper penetration the emitter is

conneted to a driver circuit.

There are three light matter interactions. They are,

1. Absorption of Infra Red ray

2. Reflectance

3. Scattering

The first interaction absorption is most

preferable[9].The output level depends on the

remaining amount of IR rays that comes out after absorption by the blood cells In contrast to other

configurations, we use transmitted light in addition

to reflected light for glucose and haemoglobin

measurement. In the transmission mode the light

traverses the whole organ (finger), and the photons

typically encounter many more glucose and

haemoglobin molecules along their paths than in

the reflection mode. This enhances the sensitivity

to glucose and haemoglobin and clinically relates

to an average over a whole organ rather than a

highly localized measurement. In addition, the

influence of local factors such as skin morphology and pigmentation is reduced.

. The output radiation is processed by the

microcontroller.The principle measurement of

Haemoglobin is based on the fact of substantial

absorption/transmission of near infrared rays through oxygenated hamoglobin(HbO2).

Fig3:IR interaction with molecules in blood

ADVANTAGE

1. No loss of blood

2. Less discomfort and pain

3. Frequent monitoring of glucose and

haemoglobin levels

4. No pricking of finger

APPLICATION

1. Continuous monitoring of glucose level.

2. Detection of pre diabetes

3. Detection of anemia,hyperglycemia and

hypoglycemia.

4. Used to get faster result

CONCLUSION

The initial problem was the use of invasive and

minimally invasive methods for blood glucose

concentration measurements. To address this problem, this project uses near-infrared as a

possible means to measure blood glucose and Hb

levels. This implementation served to be non-

invasive. To implement the near-infrared

spectroscopy, light emitting diodes that emit light

2nd


101

at 940nm and 870nm were chosen and used. To

detect the reflected light, a photodiode is used. The

peak wavelength of glucose and Hb reflective

sensors are 940nm and 870nm approximately. To

test the device, measurements are taken from

various individuals with low and high blood glucose as well as haemoglobin levels. The voltage reading from the photodiode is amplified and

filtered using a non inverting amplifier and is

converted into values by the microcontroller and

the output is viewed in the display. Using near-

infrared light of wavelength 940nm, minimum

photodiode output voltages were the same for all

individuals (1.5V). Maximum photodiode output

voltages ranges from 3 to 3.8V for the individuals.

Fig 4.1:survey on diabetic patients

Fig 4.2: survey on anemic patients

In conclusion, this project has suggested a means

for non-invasive blood glucose and haemoglobin

levels testing. Although not as accurate as present day invasive or minimally invasive techniques for

measuring blood glucose- Hb concentrations, but

the use of near-infrared light provides a means of

non-invasive measurement with less pain and

discomfort to the diabetic patients

References

1. “Design of simple non-invasive glucose measuring

device”, published in International conference on

computing electrical and electronics engineering

,2013,DOI:10.1109/ICEEE.2013.663395

2. “Near infrared LED based noninvasive blood glucose

sensor”,published in International conference in Signal

Processing & Integrated

Networks(SPIN),2014,DOI:10.1109/SPIN.2014.6777023

3. “Non invasive Sensor Technique for total haemoglobin

measurement in blood”,Journal of Industrial and

intelligent Information vol.1,no.4 dec 2013.

4. Elina A. Genina , (2012) ‘Sensing Glucose and

Other Metabolites in Skin’ Handbook of Biophotonics,

Vol.2, Chapter 55.

5. Murugesh Shivashankar, Dhandayuthapanimani(2011), ‘

A Brief Overview of Diabetes’International Journal of

Pharmacy and Pharmaceutical Sciences, Vol 3, Suppl 4.

6. A.A. Shinde et al., " Non-invasive blood glucose

measurement using NIR technique based on occlusion

spectroscopy ," International Journal of Engineering

Science and Technology (IJEST), vol. 3, No.12

December 2011.

7. O Amir, D Weinstein, M.D. Silviu Zilberman, M. Less,

D. Perl-Treves, H. Primack, A. Weinstein, E. Gabis, B.

Fikhte, and A.Karasik, “Continuous noninvasive glucose

monitoring technology based on occlusion

spectroscopy," Journal of Diabetes Science

andTechnology, vol.1, no.4, pp. 463–469,July 2007.

8. U. Timm, D. McGrath, E. Lewis, J. Kraitl and H.Ewald,

“Sensor system for non-invasive optical heamoglobin

determination,” IEEE Sensors, October, 2009.

9. G. Zonios, J. Bykowski, and N. Kollias, “Skin melanin,

hemoglobin and light scattering properties can be

quantitatively assessed INVIVO using diffuse reflection

spectroscopy,” Journal of Investigative Dermatology,

vol. 117, pp. 1452-1457, 2001.

10. A. Yao, MD and H. Liu, MD “Continuous Noninvasive

Hemoglobin Measurement,” SCA Bulletin, October

2009, vol. 8, no. 5.

11. Rajashree Doshi, Anagha Panditrao(2013),’Noninvasive

optical sensor for haemoglobin determination’

International Journal of Engineering and Research and

Applications,vol.3, Issue 2.

12. O.S. Khalil, “Spectroscopic and clinical aspects of non-

invasive glucose measurements”, Clinical Chemistry, vol.

45, no.2, pp.165-177, 1999.


102

PHYSIOTHERAPY FOR REHABILITATION USING MOTION SENSOR Sheeba Abraham 1

1,Vamsi Krishna 2

2

1, 2 Sathyabama University

ABSTRACT

Physiotherapy plays a vital role in motor rehabilitation. People affected with muscular diseases require lifelong exercises to maintain their muscle tone. This requires a lot of training on patients’ part and can be done under the supervision of physiotherapist only. So Motion-based and virtual reality game systems have been used for rehabilitation earlier but require sensors to be attached to the patient and this hinders movement. This is a huge drawback in cases where the patient has limited limb movement and as a result of the wires and sensors attached to his body, feels bound. In order to bring the patients out of the confines of hospital by allowing them to do exercises at home, we have developed a software based application that uses Kinect -the motion sensor for carrying out routine rehabilitation exercise which is easy to use and engage its user so that he can monitor his progress and thus be motivated towards recovery. It also provides feedback to the patient on his performance on the run as well as at the successful completion of the exercise routine. This allows the physiotherapists to efficiently track the performance

of the patient over time which in turn helps him to take better clinical decision.

Keywords: Motor Rehabilitation, Motion based systems, Kinect Sensor

1. INTRODUCTION

People affected by muscular diseases like

cerebral palsy, muscular dystrophy, hemiparesis

should do exercise on a regular basis in order to

maintain a healthy muscle tone. Children affected by such diseases are attended by physiotherapists

regularly to track the condition of these children.

After meeting a physiotherapist we found

that both the children and adults lacks interest in

doing the exercises regularly which leads to further

complications. So we thought of complementing

these exercises to motivate the children to do

exercises with the help of a motion sensor called

Kinect.

1.1 DISEASES AND THEIR

TREATMENTS:

We have given the detailed information

about the diseases on which we have concentrated,

their definition, causes, diagnosis and symptoms and

treatments involved:

1.1.1 Cerebral Palsy:

Cerebral palsy (CP)[7] is a disorder that affects

muscle tone, movement, and motor skills (the ability

to move in a coordinated and purposeful way). CP is

usually caused by brain damage[9] that occurs before

or during a child's birth, or during the first 3 to 5 years of a child's life.

The three types of CP are: Spastic cerebral palsy — causes stiffness and

movement difficulties

Athetoid cerebral palsy — leads to involuntary

and uncontrolled movements

Ataxic cerebral palsy — causes a disturbed sense of balance and depth perception

1.1.1.1 Causes and treatment of Cerebral Palsy

The exact causes of most cases of CP are unknown,

but many are the result of problems during pregnancy

in which the brain is either damaged or doesn't

develop normally.Problems during labour and

delivery can cause CP[8] in some cases but this is the

exception Orthopaedic surgery can help repair

dislocated hips and scoliosis (curvature of the spine),

which are common problems associated with CP.

1.1.2 Hemiparesis:

Hemiparesis is a condition that is commonly

caused by either stroke or cerebral palsy, although

it can also be caused by multiple sclerosis, brain

tumours, and other diseases of the nervous system or

brain.

1.1.2.1 Causes and treatment of Hemiparesis

Brain damage caused by head injuries, cancerous

growths in a person's brain, or disease may also lead

to the development of muscle weakness. Damage to the person's brain can lead to muscle weakness as

well. Stroke; however, is the most common reason

people develop hemiparesis. Physiatrists, Physical

Therapists, Occupational Therapists, Electrical

Stimulation, Cortical Physiatrists, Physical

Therapists, Occupational Therapists, Electrical

Stimulation, Cortical Stimulation, Botox/Baclofen,

Motor Imagery, Modified Constraint-induced

Therapy, Medical science has created, or is looking

into, some promising new treatments for people with

hemiparesis that can also help people who have


103

experienced a stroke to improve movement in their

legs and arms after the initial stroke.

1.1.2.2 Forms of Hemiparesis

• Right-sided Hemiparesis: Involves injury to

the left side of the person's brain. The left side of a person's brain controls speaking

and language.

• Left-sided Hemiparesis: Involves injury to

the right side of the person's brain, which

controls learning processes, certain types of

behaviour, and non-verbal communication.

• Ataxia: Injury to the lower portion of a

person's brain may affect their body's ability

to coordinate movement.

• Pure Motor Hemiparesis: Pure motor

hemiparesis is the most common type of

hemiparesis. • Ataxic Hemiparesis Syndrome: Ataxic

hemiparesis syndrome involves weakness or

clumsiness on one side of a person's body.

2. METHODOLOGY

2.1 KINECT- THE MOTION SENSING

DEVICE

We have used Kinect as a tool to implement

our ideas about complementing rehabilitation. Kinect

is a motion sensor which tracks the full skeleton and

provides feedback about the movement of the person

with accuracy.

2.2 THE MOTION SENSOR- KINECT:

We used Kinect as the tool to achieve our

goal and developed programs that are compatible

with the Kinect and provided the detailed

information about the working of Kinect.

Fig 2.1 Kinect and its parts

Fig 2.2. Kinect Tracks Wholebody Skeleton

2.2.1 Introduction:

The device provides a natural user interface (NUI) [1] that allows users to interact intuitively and

without any intermediary device, such as a controller.

The Kinect system [2] identifies individual players

through face recognition and voice recognition. A

depth camera, which “sees” in 3-D, creates a skeleton

image of a player and a motion sensor detects their

movements

2.2.2 Construction of Kinect :

The Kinect sensor is a horizontal bar

connected to a small base with a motorized pivot and

is designed to be positioned lengthwise above or below the video display. The device features an

"RGB camera, depth sensor and multi-array

microphone running proprietary software", which

provide full-body 3D motion capture, facial

recognition and voice recognition capabilities.

The depth sensor consists of an infrared

laser projector [2] combined with a monochrome

CMOS sensor, which captures video data in 3D under

any ambient light conditions.

2.2.3 Working of Kinect:

The Kinect uses the structured light and machine learning[1] to infer the body position.

Inferring body position is a two stage process:

• It computes the depth map using structured

light.

• It uses machine learning for inferring the

body position.

Stage 1:

• The depth map is constructed by analysing a

speckle pattern of infrared laser light

• The Kinect uses an infrared projector and

sensor; it does not use its RGB camera for depth computation

The technique of analysing a known pattern

is called structured light.

• Structured light general principle: project a


104

known pattern onto the scene and infer

depth from the deformation of that pattern

on to the scene and inferdepth from focus

uses the principle that stuff that is more

blurry is further away.

• The Kinect dramatically improves the accuracy of traditional depth from focus

• The Kinect uses a special (“astigmatic”) lens

[2]with different focal length in x- and y-

directions

• A projected circle then becomes an ellipse

whose orientation depends on depth

• Depth from stereo uses parallax

• Kinect analyses the shift of the speckle

pattern by projecting from one location and

observing from another.

Stage 2:

Stage 2 has 2 sub stages (use intermediate “body parts” representation)

Stage 2.1: Start with 100,000 depth images with known

skeletons (from a motion capture system)

For each real image, render dozens more

using computer graphics techniques

Use computer graphics to render all

sequences for 15 different body types, and

vary several other parameters

Thus obtain over a million training

examples.

Stage 2.2

Transforms the body part image into a

skeleton The mean shift algorithm is used to robustly

compute modes of probability distributions

Mean shift is simple, fast, and effective.

2.3 THE SOFTWARE LANGUAGES AND

ALGORITHMS USED:

The languages used for programming are C ,

XML. The software packages used for hardware

aspects are Unity 3d and Blender [3].

2.3.1 Unity 3d:

Floor- 3D area on which skeleton moves

Image viewer- shows image of patient

Main camera- window that displays the skeleton and image viewer

User Interface

Zig skeleton- represents head, neck, torso,

waist, collar, shoulder, elbow, wrist, hand,

fingertip, hip, knee, ankle, foot.

2.3.2 User interface and algorithms for camera views

and skeletal pose:

The camera is shown as a label under which

buttons for different views of the camera are given.

Right side camera,

Front camera

Top camera.

2.3.3 Algorithms for camera views and skeletal pose:

Right side camera:

Enter camera option

If right side camera is selected, main camera

position is transformed as (10,0,0) and Euler’s angle as (0,270,0)

The image viewer position is transformed as

(-10,5,10) and Euler angles as (0,90,0) and

image viewer scale as (10,10,0.1f)

Front camera:

Enter camera option

If front camera is selected the main camera’s

position is transformed as (0,0,10) and Euler

angles as (0,180,0)

The image viewer position is transformed as (-10,5,-10) and Euler angles as (0, 0,0) and

image viewer scale as (10,10,0.1f).

Top camera:

• Enter the camera option

• top camera is selected the main camera’s

position is transformed as (0,10,0) and Euler

angles as (90,0,0)

• The image viewer position is transformed as

(5,0,2.5f) and Euler angles as (270,0,180)

and image viewer scale as (5,5,0.1f).

Skeletal pose algorithm:

• Enter the skeletal pose

• If mirroring off button is clicked zig

skeleton mirror is false and the skeleton

does not mirror the image of the person

• Else if mirroring on button is clicked zig

skeleton mirror is true and the skeleton

mirrors the image of the person.

Algorithm for setting elevation in Kinect:

• Enter the angle to which Kinect must be adjusted


105

• Select the ‘Set Elevation’ button

• The Kinect adjusts to specified angle

User interface, algorithm for therapy and type of

exercise selection:

Cerebral palsy:

Algorithm:

• Select the cerebral palsy button under the

label therapy

• Select the required exercise

1.shoulder abduction

2.horizontal adduction

3.shoulder flexion and extension

4.elbow flexion and extension.

Algorithms for exercises: Cerebral Palsy:

a b

c d

a)Shoulder abduction b)Horizontal adduction

c)Shoulder flexion and extension d)Elbow flexion and extension

Fig. 2.3 Exercise for cerebral Palsy

3. RESULTS AND DISCUSSION

We have developed the programs and were

able to execute them with kinect. We got the

output and attached the output screen shots for all

8 exercises.

3.1 Shoulder Abduction:

• Stand 6 feet away from the sensor • Raise the arm to 90 degrees and bring back

to rest position (0 degrees)

• The counter is triggered and increases by 1

• The exercise is repeated till counter reaches

10 after which it resets to 0.

Fig 3.1 Output for Shoulder Abduction

3.2 Horizontal Adduction:

• Stand 6 feet away from the sensor

• Raise the arm to 90 degrees and bring back

to rest position (0 degrees)

• The counter is triggered and increases by 1 • The exercise is repeated till counter reaches


Fig 3.2 Output for Horizontal Adduction

3.3 Shoulder Flexion And Extension


• Raise the hand to 90 degrees and bring back to rest position (0 degrees)

• The counter is triggered and increases by 1

• The exercise is repeated till counter reaches


n

Fig 3.2 Output for Shoulder Flexion and extension

3.5 Elbow Flexion and Extension:


• Flex the hand to 90 degrees and bring back

to rest position (0 degrees) • The counter is triggered and increases by 1

• The exercise is repeated till counter reach10

after which it resets to 0.

•

Fig 3.4 Output for Elbow Flexion and Extension

on

4. SUMMARY

The main objective of our paper is to bring a


106

change in the exercise routine of the physically

challenged children and develop software that can

present the usual exercises in an unusual form which

motivates and helps the children towards better

recovery. The people affected with muscular diseases

need therapeutic attention regularly for avoiding worsening of disease.

The main tool that we use to implement the

software is Kinect. The Kinect is a motion sensing

device that tracks the full body skeleton and moves

according to the user. The patient gets a feedback

about their performance in points after completing

each exercise. And every exercise has repetitions too.

As the patient completes one level they are

challenged with progressive difficult levels to test

their capability which helps them remain active. If

the therapist is not satisfactory with the results the patients can repeat their exercise.

The software languages that we used to

program are: c# and the game engines which we used

to design the character for Kinect and implement the

exercises are unity 3d and blender. The Zigfu pluggin

is used to integrate the entire exercises into Kinect.

5. CONCLUSION The exercises for children affected with

cerebral palsy and were collected after interviewing

the therapists and observing the affected children

directly. We have used c# for developing the exercise

programs and integrated it with unity –game engine

using zigfu pluggin.

The software is user friendly and provides

points and counts after completion of each exercise

and we have included light background music throughout exercise and a cheering sound while

displaying points to motivate the user to do the

exercises better.. This can be used for assisting

therapists and can be implemented in hospitals,

homes, rehabilitation centres and schools were many

patients need therapeutic attention throughout their

life. The efforts were really worth and the results

were highly satisfactory and beneficial for both

patients and the therapists as well.

6. REFERENCES

[1]. Jarret webb, James ashly, “Beginning Kinect Programming

with the Microsoft Kinect SDK”,Apress, pages 310,2012.

[2]. Sean kean, Jonathan hall, Phoenix perry, “Meet the Kinect:

An Introduction wto Programming Natural User Interfaces

(Technology in Action)”, publisher: wPaul manning, editor:

Jonathan Gennick. Pages 210.2011 20112

[3]. Hamilton, Naomi "The A-Z of Programming Languages:

C#", Computerworld,October 1, 2008.

[4]. O'Sullivan, S."Ch. 12: Stroke". In O'Sullivan, S.; Schmitz, T.

Physical Rehabilitation (5th ed) Philadelphia: F.A. Davis. pp.

705–769, (2007).

[5]. Patricia A. Downie,”Cash's Textbook of neurology for

physiotherapists”, wPublisher:Lippincott, Editor:Patricia A.

Downie, 653 pages,5 Aug 2008.

[6]. Lagerqvist, J. & Skargren, E. "Pusher syndrome: reliability,

validity, and sensitivity to change of a classification

instrument".Advances in Physiotherapy 8154–160, 2006.

[7]. Joan ross,”I Can't Walk but I Can Crawl: A Long Life with

Cerebral Palsy “(Lucky Duck Books), Sage Publications

(CA) 232 pages, October 18th 2005.

[8]. Sophie Levitt, “Treatment of Cerebral alsy and Motor

Delay”, Published by wWiley-Blackwell Paperback, 360

pages,2003.

[9]. Elaine Geralis ,”Children with Cerebral Palsy: A Parents'

Guide”, Woodbine House,Paperback, 481 pages. (first

published December 1991), January 1st 1998.

[10]. Grace E. Woods, “Infantile Cerebral Palsy”, Clinical Press

Ltd, Hardcover, pages 120. February 2nd

1995.

http://www.computerworld.com.au/article/261958/-z_programming_languages_c

http://www.computerworld.com.au/article/261958/-z_programming_languages_c

http://en.wikipedia.org/wiki/Computerworld

http://www.goodreads.com/author/show/1694279.Sophie_Levitt

http://www.goodreads.com/author/show/487188.Elaine_Geralis

http://www.goodreads.com/author/show/1677245.Grace_E_Woods


107

PALM PRINT BASED SECURITY SYSTEM FOR LOCKERS

Angelena Mathias 1 1, S. Caroline 2

2

St. Xavier’s Catholic College of Engineering, Nagercoil, India

ABSTRACT

According to ancient Greek scripts Biometrics means study of life. Biometric studies commonly include palm print, face, iris, voice, signature, hand geometry recognition and verification. Among the available biometric traits Palm Print proves to be one of the best traits providing good mismatch ratio and also reliable. The present scenario to operate a bank locker is with locks which are having keys. By this we can’t say that we are going to provide good security to our lockers. To provide perfect security and work easier, two different technologies Embedded Systems and Biometrics are used. Data acquisition is done by a scanner and images are stored in database. It has in-built ROM, DSP

and RAM where we can store up to 100 users palm prints. This module operates in 2 modes they are Master and User mode. In User mode, the scanned images are verified with the stored images. The images of the persons who are authorized to enter into the room will be stored in the module with a unique id. To prove that the persons are authorized to enter that area they need to scan their images. After the scanning has been completed the door automatically unlocks with the help of electric lock. If an unauthorized person tries to scan his image then the door doesn’t open for him.

Keywords— SVM, Euclidean distance, SIFT, Median Filter.

I. INTRODUCTION

Biometrics refers to the automatic identification

or identity verification of living persons using their

enduring physical or behavioral characteristics.

Many body parts, personal characteristics and

imaging methods have been suggested and used for

biometric systems such as fingers, hands, feet,

faces, eyes, ears, teeth, veins, voices, signatures,

typing styles, gaits and odors. As physical

characteristics are constant they do not change over

time and are also difficult to fake or change on

purpose. Physiological approaches include

fingerprints, iris, retinal scans, hand, finger, face,

ear geometry, hand vein, nail bed recognition, DNA

and palm prints. It is also a standardised model with

high accuracy. The cost of a palm print system is

lower than a finger print system. This is because in

palm print technique we use low resolution images

hence the cost of the capturing device and also the

memory requirement is less. It contains more

information and also hardly affected by age and

accessories. It is used in forensics, offices, and

departmental stores for security purpose.

Depending on where the biometrics is deployed,

the applications can be categorized in the following

five main groups: forensic, government,

commercial, health-care and traveling and

immigration. However, some applications are

common to these groups such as physical access,

PC/network access, time and attendance etc.


The block diagram of the palm print security

system is shown in Fig. 1. The images are collected,

preprocessed and feature extracted and stored in the

database. The sample image is then preprocessed,

feature extracted and it is then matched with the

database image. If the sample matches the image in

database then the access is granted.

Fig. 1 Block diagram of Palm print security system


108

A. Palm print Image Acquisition

It is an action of retrieving an image from some

source, usually hardware based. Performing image

acquisition is always the first step in image

processing. The image that is acquired is completely

unprocessed and is the result of whatever hardware

was used to generate it.

Fig. 2 shows a flatbed scanner which is a

commonly used image capture device, the price of

the capture device is very low and the reliability and

facility of the device could also be ensured. The

flatbed scanner has its own lighting system and is

not affected by the surrounding light, which saved

design time for establishing a stable lighting system.

Fig. 2 Palm print Image Acquisition System

There are various kinds of flatbed scanners on

the market and we could easily choose a desired

flatbed scanner that is small and low in price, which

could capture a high-quality image from a user’s

palm. In general, we could design the capture

device with a good balance of cost and quality to

meet the above requirements.

B. Pre-Processing

Preprocessing is done to enhance the visual

appearance of the image. The datasets manipulation

is improved. The palm print image is formed of

principal lines, wrinkles, minutiae points, singular

points and texture. These palm print images, stored

in database, are not of quality, therefore these

images must undergo some pre-processing It

involves the following steps

Resizing

The input image may be of any size. It is

first resized to a size of (256 × 256 pixels). This is

to make all the palm prints of the same size and to

reach the result with fast response.

Median Filtering

Palm print image processing can be

implemented by using some filters and

enhancement in order to prepare it to the feature

extraction steps. Median filter is a nonlinear digital

filtering technique often used to remove noise. It is

used to remove speckle noise and salt and pepper

noise.

Histogram Equalization

It is a technique for adjusting image

intensities to enhance contrast. This method usually

increases the global contrast of many images,

especially when the usable data of the image is

represented by close contrast values.

Image Thresholding

It is simplest method of image segmentation.

It is a nonlinear operation that converts a gray scale

image into binary images. It is an effective way of

partitioning an image into a foreground and

background.

Detecting key points (SIFT)

SIFT key points of objects are first extracted

from a set of reference images and stored in a

database. An object is recognized in a new image by

individually comparing each feature from the new

image to this database and finding candidate

matching features based on Euclidean distance of

their feature vectors. From the full set of matches,

subsets of key points that agree on the object and its

location, scale and orientation in the new image are

identified to filter out good matches. The

determination of consistent clusters is performed

rapidly by using an efficient hash


109

table implementation of the generalized Hough

transform. Each cluster of 3 or more features that

agree on an object and its pose is then subject to

further detailed model verification and subsequently

outliers are discarded. Finally the probability that a

particular set of features indicates the presence of an

object is computed, given the accuracy of fit and

number of probable false matches. Object matches

that pass all these tests can be identified as correct

with high confidence.

C. Sharpening

The high pass filtered version of the Pre-

Processed image is added to the Pre-Processed

image in Image Sharpening. Sharpened image

highlights the edges and details of the Pre-processed

image.

D. Feature Extraction

Feature selection is one of the most

important tasks in data mining area, with methods

which allows determining the most relevant features

for pattern recognition. A suitable subset of features

is found when it permits synthesizing the similarity

of the pattern within its class and dissimilarity

amongst other different classes. The features which

give predominant difference between normal and

infected cells are identified and used for training

purpose. The selected features are geometrical,

color and statistical based. The mathematical

morphology provides an approach to the processing

of image based on shape. The statistical features use

gray level histogram and saturation histogram of the

pixels in the image and based on such analysis, the

mean value and variance are treated as the features

and are calculated. Texture is a property of natural

image like smoothness, coarseness and regularity of

a region. Though texture is an intuitive concept,

there is no commonly accepted definition for it.

Texture classification is one of the four

problem domains in the field of texture analysis

such as synthesis, classification, segmentation and

shape from texture. An image texture is a set of

metrics calculated in image processing designed to

quantify the perceived texture of an image. Image

Texture gives us information about the spatial

arrangement of color or intensities in an image or

selected region of an image. Image textures can be

artificially created or found in natural scenes

captured in an image. Image textures are one way

that can be used to help in classification of images.

To analyze an image texture in computer graphics,

there are two ways to approach the issue: Structured

Approach and Statistical Approach. Texture is

generated from the gray scale image matrices of the

red, green and blue components, as well as the

intensity component from the hue-saturation-

intensity image space. First order features, based on

the image histograms are used.

Average gray level or mean

The mean of the set of x values can be

denoted by , Mean of an image can be defined as

the sum of all pixel values to the total number of

pixels.

Where

- Sum of all x values

- Number of x values

Variance

The variance of a random variable X is its

second central moment, the expected value of the

squared deviation from the mean.

Where

– Mean of the image

SVM

SVM is one of the methods which the

statistical learning theory can be introduced to

practical application. It has its own advantages in

solving the pattern recognition problem with small

samples, nonlinearity, and higher dimension. SVM

can be easily introduced into learning problem such

http://en.wikipedia.org/wiki/Central_moment

http://en.wikipedia.org/wiki/Expected_value

http://en.wikipedia.org/wiki/Mean


110

as palm classification. As for the multiple

classification case, the basic idea is to transform the

problem into a 2-class problem, which is usually

implemented using a combined classifier. A poll is

carried out for all these 2 class classifiers and the

class with the most polls is determined as the class

that the point being analysed belongs to. When the

sample x is fed into the 2-class classifier

constructed from the class sample and the

class sample, the classifying function is then given

as

Where

s - Support vector

- Location of the hyper plane

-Vertical vector on the classifying hyper plane

Sign - is the signed function

- Kernel function

The central part of palm is consisting of three

ridges, flexion and secondary creases. The flexion

creases are otherwise called principal lines and the

secondary creases as wrinkles. These flexion and

the major secondary creases are formed between the

3rd and 5th months of pregnancy and superficial

lines appear after we are born. Even though flexion

is genetically dependent, most of other creases are

not. Even identical twins are having different palm

prints. These non-genetically deterministic and

complex patterns are very much useful in personal

identification.

E. Matching

The palm print features extracted are

compared with the sample images by the calculation

of Euclidean distance between the values. When

there is minimum Euclidean distance between the

features of the images, the palm prints are classified

as matching or mismatching with the database and

hence used for personal authentication purpose.

III. RESULTS

The simulation of the palm feature extraction and

matching is done using MATLAB. The

performance metrics such as mean, variance and

Euclidean distance is calculated. The Palm images

are collected and stored in the Database and their

corresponding values are also calculated. There are

five major steps carried out in this proposed system.

They are image acquisition, preprocessing,

Sharpening, Feature extraction and matching. Palm

image of any size is captured and preprocessing is

done. The results of each process are shown

separately and matching is done finally.

Fig. 3 Sample image matches the database image

Fig. 3, if the sample image matches the

image in the database then the access is granted and

the locker can be opened. Fig. 4 if the sample image

does not match the images in the database then the

system cannot be accessed and we get an Access

Denied message and the locker cannot be opened.


111

Fig. 4.14 Sample image do not match the database

image

IV. DISCUSSION

The experimental result for image recognition

provides more accurate results and they are used for

the further development of a fully automated palm

print-based security system with high performance

in terms of effectiveness, accuracy, robustness, and

efficiency.

V. CONCLUSION

The proposed method gave an excellent

performance even if the palm print image is

contaminated with noise, contrast quality, size and

rotation. The feature extraction of palm prints based

on the textural analysis using MATLAB is an

efficient technique when compared to other

methods of palm print classification.

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Proceedings of the

International Conference on Bio Signals, Images and Instrumentation

ICBSII 2015

Biomedical Engineering is a field of study that integrates two dynamic professions, Medicine

and Engineering. It has recently established itself as an independent field with the objective

of assisting medicine towards the betterment of society, through research.

Being an interdisciplinary science, it has associations with various other subjects such as

Electrical Engineering, Mechanical Engineering, Chemical Engineering and Biotechnology.

The spectrum of Bio-medical research aims to unite these disciplines in synergy, leading to

new possibilities thus enabling the development of technology that could save lives.

The International Conference on Bio Signals, Images and Instrumentation (ICBSII) was

conceived with the thought of bringing together scientists, engineers and researchers from

various domains all over the world.it has been a platform where some of the greatest minds of

the country and abroad could interact, exchange ideas and work together towards a common

goal.

Research papers were received from diverse areas such as Physiological Modelling, Medical

Imaging, Medical Robotics, Biomechanics, Biomedical Instrumentation and Nano-materials

amounting to a total of 152 papers. After a rigorous review process by an expert review

committee, 40 papers that displayed quality in idea and work were selected for final

presentation at the conference.

This conference is the fruit of a vision of the Management, faculty and students of the

Department of Biomedical Engineering, SSN College of Engineering who worked

unanimously towards materializing it and were instrumental in its success. Dr. A. Kavitha,

Associate Professor and Head of Bio-Medical Engineering has over 17 years of teaching

experience. She has 13 years of research experience in the field of Biomedical

Instrumentation, Medical Image Processing and Respiratory mechanics. Her current research

interest include Machine learning techniques, Bio-Medical Signal and Image Processing,

Statistical learning theory and Mathematical modelling in cognitive neuroscience.

The department of Biomedical Engineering, since its inception in 2005, has been a pioneer in

the field of biomedical technology, instrumentation, and administration. The department has

excellent infrastructure, experienced faculty members and motivated students. It also has

collaborations with several industries such as L&T Medical System, Texas Instruments,

National Instruments, Materialize, Chettinad Hospitals and Sri Ramachandra Medical

College.

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