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An Efficient Brain Tumor Extraction from MRI Images using Entropy Measures 2014-15

Poornima University, Jaipur M. Tech. (CE) Page 1

Chapter1: Introduction

Image processing is an active research area in which medical image processing is a

highly challenging field techniques are used to process of creation of visual

representation of the inner portions of the human body for medical diagnosis.

Information is conveyed through images. Image processing is a process where input

image is processed to get output also as an image. Main aim of all image processing

techniques is to recognize the image or object under consideration easier visually. All

the images used in todays world are in the digital format. Medical images are images

that show the physical attributes distribution. Medical imaging modalities as in MRI,

CT scan mostly depend on computer technology to generate or display digital images of

the internal organs of the human body which helps the doctors to visualize the inner

portions of the body. CT scanner, Ultrasound and Magnetic Resonance Imaging took

over conventional x-ray imaging, by allowing the doctors see the body's third

dimension. Brain tumor is a serious life altering disease condition. Image segmentation

plays a significant role in image processing as it helps in the extraction of suspicious

regions from the medical images. For this the segmentation of brain MRI images are

done for detection of Tumor location. Magnetic Resonance Imaging is an important

diagnostic imaging technique utilized for early detection of abnormal changes in tissues

and organs. Tumor is an uncontrolled growth of tissues in any part of the body. Most

Research in developed countries show that the number of people who have brain tumors

were died due to the fact of inaccurate detection. However this method of detection

resists the accurate determination of stage &size of tumor. To avoid that a computer

aided method for segmentation (detection) of brain tumor based on the various entropy

measures has been used.

1.1 Brain anatomy overview

BRAIN, the central processing unit of human body, is a soft, delicate and spongy mass

of tissues. It is a steady place for signals to enter and being processed. The brain directs

the things we choose to do (like walking and talking) and the things our body does without

thinking (like breathing). The brain is also in charge of our senses (sight, hearing, touch, taste,

and smell), memory, emotions, and personality. The human brain which functions as the

center for the control of all the parts of human body is a highly specialized organ that

allows a human being to adapt and endure varying environmental conditions. The



human brain enables a human to articulate words, execute actions, and share thoughts

and feelings. In this section the tissue structure and anatomical parts of the brain are

described to understand the purpose of this study.

So, before dealing with the brain tumors and MRI Images, the basic structure of human

brain must be well understood.

Fig. 1.1 The Major Subdivision Of Human Brain [19]

Following are the main parts of the human brain:

1.1.1 Brainstem: It is the lower extension of the brain where it connects to the spinal

cord. Neurological functions located in the brainstem include these necessary for

survival (breathing, digestion, heart rate, blood pressure) and for arousal (being awake

and alert). The brainstem is the pathway for all fiber tracts passing up and down from

peripheral, nerves and spinal cord to the highest parts of the brain.

1.1.2 Cerebellum: The portion of the brain (located at the back) which helps coordinate

movement (balance and music coordination).

1.1.3 Frontal Lobe: It is the front part of the brain and is involved in planning,

organizing, problem solving, selective attention, personality and a variety of higher

cognitive functions including behavior and emotions.

1.1.4 Occipital Lobe: It is the region in the back of the brain which processes visual

information. It also contains association areas that help in the visual recognition of

shapes and colors.



1.1.5 Parietal Lobe: One of the two parietal lobes of the brain are located behind the

frontal lobe at the top of the brain.

1.1.6 Temporal Lobe: There are two temporal lobes, one on each side of the brain

located at above the level of ears. These lobes allow a person to tell one smell from

another. They also help in sorting new information and are believed to be responsible

for short term memory.

The human brain which functions as the center for the control of all the parts of human

body is a highly specialized organ that allows a human being to adapt and endure

varying environmental conditions. The human brain enables a human to articulate

words, execute actions, and share thoughts and feelings.

1.2 Brain Tumors

Under certain conditions, brain cells grow and multiply uncontrollably because for some

reasons the mechanism that control normal cells is unable to regulate the growth of the

brain cells. The abnormal mass of brain tissue is the brain tumor that occupies space in

the skull and interrupts the normal functions of brain and creates an increasing pressure

in the brain. Due to increased pressure on the brain, some brain tissues are shifted,

pushed against the skull or are responsible for the damage of the nerves of the other

healthy brain tissues.

Scientists have classified brain tumor according to the location of the tumor, type of

tissue involved whether they are noncancerous or cancerous. The site of the origin

(primary of secondary) and other factors involved. World Health Organization (WHO)

classified brain tumor into 120 types. This classification is done on the basis of the cell

origin and the behavior of the cell from less aggressive to more aggressive behavior.

Even, some tumor types are graded ranging from grade I (less malignant) to grade IV

(more malignant). This signifies the rate of the growth despite of variations in grading

systems which depends on the type of the tumor.

Grade I: The tissue is benign. The cells look nearly like normal brain cells, and they

grow slowly. This type of brain Tumors are rare in adults.

Grade II: The tissue is relatively slow growing and sometimes spread to nearby normal

tissues and become malignant. The cells look less like normal cells than do the cells in a

Grade I tumor.

Grade III: These are the malignant tissue and have cells that look very different from

normal cells. The abnormal cells are actively growing (anaplastic).



Grade IV: These cover the most malignant tissue and have cells that look most

abnormal and tend to grow quickly. Tumor forms new blood vessels to maintain rapid

growth.

Primary brain tumors are the tumors that originated in the brain and are named for the

cell types from which they originated. They can be benign (non-cancerous) and

malignant (cancerous). Benign tumors grow slowly and do not spread elsewhere or

invade the surrounding tissues. However, occupying a short space, even the less

aggressive tumor can exercise much pressure on the brain and makes it dysfunctional.

Conversely, more aggressive tumors can grow more quickly and spread to other tissues.

Each of these tumors has unique clinical, radiographic and biological characteristics.

Secondary brain tumors originate from another part of the body. These tumors consist of

cancer cells somewhere else in the body that have metastasized or spread to the brain

The most common cause of secondary brain tumors are: lung cancer, breast cancer,

melanoma, kidney cancer, bladder cancer, certain sarcomas, and testicular and germ cell

tumors.

1.3 MRI Images

Raymond V. Damadian invented MRI in 1969 and was the first person to use MRI to

investigate the human body. Eventually, MRI became the most preferred imaging

technique in radiology because MRI enabled internal structures be visualized in some

detail. In MRI signal processing considers signal emissions. The imaging process does

not involve the use of ionizing radiation and hence does not have the associated

potential harmful effects. It is a tomographic imaging technique that produces images of

internal physical and chemical characteristics of an object from externally measured

Nuclear Magnetic Resonance (NMR) signals. MR imaging is based on the well-known

NMR phenomenon. MR signals used for image formation come directly from the object

itself. With MRI, good contrast between different soft tissues of the body can be

observed. This makes MRI suitable for providing better quality images for the brain, the

muscles, the heart and cancerous tissues compared with other medical imaging

techniques, such as Computed Tomography (CT) or X-rays. Magnetic Resonance

Imaging (MRI) used in radiology to investigate the anatomy and physiology of the body

in both health and disease. MRI scanners use magnetic fields and radio waves to form

images of the body. The technique is widely used in hospitals for medical diagnosis,

staging of disease or for accurate detection, shape and size of the tumor.



Figure1.3 shows that a patients head is examined from three plans in a clinical

diagnosis which are axial plane, sagittal plane and coronal plane.

Fig. 1.3 Head Examined in a Clinical Diagnosis [21]

The above figure 1.3 shows that the patients head is examined from three planes in a

clinical diagnosis which are Axial plane, Sagittal Plane, Coronal Plane. Furthermore,

T1-weighted brain MRI Images from various planes are shown below.

Fig. 1.3.1 (a) Axial Plane (b) Sagittal Plane (c) Coronal Plane [21]

The above figure 1.3.1 shows that the patients head is examined from three planes in a

clinical diagnosis which are Axial plane, Sagittal Plane, Coronal Plane.

Generally, T scan or MRI that is directed into intracranial cavity produces a complete

image of brain. This image is visually examined by the physician for detection and

diagnosis of brain tumor. However this method of detection resists the accurate

determination of stage & size of tumor. To avoid that, this project uses computer aided

method for segmentation (detection) of brain tumor based on the combination of two

algorithms. This method allows the segmentation of tumor tissue with accuracy and



reproducibility comparable to manual segmentation. In addition, it also reduces the time

for analysis. At the end of the process the tumor is extracted from the MRI Image and

its exact position and the shape also determined. The stage of the tumor is displayed

based on the amount of area calculated from the cluster.

1.4 The Image Segmentation

In computer vision, image segmentation is the process of partitioning a digital image

into multiple segments (sets of pixels, also known as super pixels). The goal

of segmentation is to simplify and/or change the representation of an image into

something that is more meaningful and easier to analyze. Segmentation is typically used

to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image

segmentation is the process of assigning a label to every pixel in an image such that

pixels with the same label share certain characteristics. The result of image

segmentation is a set of segments that collectively cover the entire image, or a set of

contours extracted from the image (see edge detection). Each of the pixels in a region is

similar with respect to some characteristic or computed property, such as color,

intensity, or texture. Adjacent regions are significantly different with respect to the same

characteristic. When applied to a stack of images, typical in medical imaging, the

resulting contours after image segmentation can be used to create 3D reconstructions

with the help of interpolation algorithms. This is typically used to identify objects or

other relevant information in digital images.

The purpose of segmentation is to separate image information into clear meaningful

parts by placing boundaries separating the area of the healthy brain from the area of the

cancerous and tumorous brain. The objective of image segmentation is to cluster pixels

into prominent image region. Segmentation must not allow regions of the image to

overlap. Image segmentation can be classified into three categories: spatial clustering,

split and merge schemes, and region growing schemes.

1.5 Application of Image Segmentation

There are various applications of the image segmentation that are described below:

1.5.1 Content Based Image Retrieval: Content-Based Image Retrieval (CBIR), also

known as Query. By Image Content (QBIC) and Content-Based Visual Information

Retrieval (CBVIR) is the application of computer vision techniques to the image

retrieval problem, that is, the problem of searching for digital images in large databases.



Content-based means that the search analyzes the contents of the image rather than

the metadata such as keywords, tags, or descriptions associated with the image. The

term content in this context might refer to colors, shapes, textures, or any other

information that can be derived from the image itself. CBIR is desirable because

searches that rely purely on metadata are dependent on annotation quality and

completeness.

1.5.2 Machine Vision: Machine Vision (MV) is the technology and methods used to

provide imaging-based automatic inspection and analysis for such applications as

automatic inspection, process control, and robot guidance in industry.

1.5.3 Medical Imaging: Medical imaging is the technique and process of creating

visual representations of the interior of a body for clinical analysis and medical

intervention. Medical imaging seeks to reveal internal structures hidden by the skin and

bones, as well as to diagnose and treat disease. Medical imaging also establishes a

database of normal anatomy and physiology to make it possible to identify

abnormalities. Although imaging of removed organs and tissues can be performed for

medical reasons, such procedures are usually considered part of pathology instead of

medical imaging. Locate tumors and other pathologies.

Measure tissue volumes

Diagnosis, study of anatomical structure

Surgery planning

Virtual surgery simulation

Intra-surgery navigation

1.5.4 Object Detection: Object detection is a computer technology related to computer

vision and image processing that deals with detecting instances of semantic objects of a

certain class (such as humans, buildings, or cars) in digital images and videos. Well-

researched domains of object detection include face detection and pedestrian detection.

Object detection has applications in many areas of computer vision, including image

retrieval and video surveillance.

Pedestrian detection

Face detection

Brake light detection

Locate objects in satellite images (roads, forest, crops, etc.)



1.6 Introduction to Brain Tumor Segmentation

Brain Tumor Segmentation is the process of partitioning a digital image into multiple

segments (sets of pixels, also known as super pixels). The goal of segmentation is to

simplify and/or change the representation of an image into something that is more

meaningful and easier to analyze. Image segmentation is typically used to locate objects

and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is

the process of assigning a label to every pixel in an image such that pixels with the same

label share certain characteristics. Segmentation may also depend on various features

that are contained in the image. It may be either color or texture. Before de-noising an

image, it is segmented to recover the original image. The main motto of segmentation is

to reduce the information for easy analysis. Segmentation is also useful in Image

Analysis and Image Compression. In order to investigate and present the image

segmentation, different entropy measures for threshold selection purpose is used.

1.7 Difficulties in Segmentation of Brain MRI

Cortical segmentation has not been made fully automated and operated at high speed

because of the reliability of the MRI with regards to the homogeneity of its magnetic

field. The problems of MRI include:

1. Noise: random noise connected with MR imaging system. This is known to have

a Rician distribution.

2. Intensity inhomogeneity also called bias field or shading artifact: the non-

uniformity in the radio frequency (RF) field during data collection results in

shading effect.

3. Partial volume effect: In this type of effect more than one type of class or tissue

occupies one pixel or voxel of an image. The pixels or voxels are called mixels.

Segmentation of MRI outputs are normally done by medical experts and requires

processes which consume time. As the images of tumor tissues from different patients

contain many diverse appearance and gray level intensities and frequently look similar

to normal tissues, the process of automation for segmentation of MRI outputs faces

many challenges. One of these challenges is overcome by utilizing prior information

about the appearance of normal brain when performing classification from a multi-

dimensional volumetric features set. This is tantamount to using a statistical model for

tumor and normal tissue of the same feature set.



1.8 Thresholding

Thresholding is the simplest method of image segmentation. Thresholding is a process

of cinverting a grayscale input image to a bi-level image by using an optimal threshold.

The simplest thresholding methods replace the each pixel in an image with a black pixel

if the image intensity Ii,j is less than some fixed constant T that is, Ii,j



1.11.3 Entropy Based Methods: It gives the result in algorithms that use the entropy of

the foreground and background regions and the cross-entropy between the original and

binarized image, etc.

1.11.4 Object Attribute -Based Methods: It searches a measure of similarity between

the gray-level and the binarized images, such as fuzzy shape similarity, edge

coincidence, etc.

1.11.5 Spatial Methods: It uses higher-order probability distribution and/or correlation

between pixels.

1.11.6 Local Methods: It adapts the threshold value on each pixel to the local image

characteristics. In these methods, a different T is selected for each pixel in the image.

Now-e-days, speed of computation is no longer an issue for researchers. Therefore, the

focus is directed toward improvement of information from images obtained through the

slice orientation and perfecting the process of segmentation to get an accurate picture of

the brain tumor.

Chapter 2 deals with details of information extracted from research paper related to brain

tumor detection, segmentation, enhancement and feature extraction and its analysis

leading to strengths, weaknesses and gaps in the published research.

Chapter 3 discusses the theoretical aspects of the targeted work and design details of the

technique used by the researchers and also the main approach used in this dissertation.

Chapter 4 elaborates on the proposed system design and its implementation. It includes

details of different tools and techniques and the simulation of the software used. The

architecture description is presented to support the process flow of the work in thesis.

Chapter 5 deals with the experimental results and analysis of the brain tumors through

MRI image segmentation. It also includes the comparison between Shannon and Non-

Shannon entropy measure along with the current comparison with status of the

researcher performance.

Finally, the conclusion is made in chapter 6, summarizing the challenges, proposed

technique and its effectiveness as compared to earlier work.

Next chapter shall discuss about the comprehensive review of the methods and

techniques used to detect brain tumor through MRI image segmentation leading to

problem statement and objective.



Chapter2: Literature Review

A literature review is necessary to identify a problem and the stages of its solution. A

proper literature review provides a solid background for a noble research work.

Literature survey includes the study of various sources of literature in the area of

research. It includes finding the related material from magazines, books, research

articles, scientific research papers published in various conferences, journals &

transactions. Study and understanding the literature other than scientific research papers

is a bit easy as it elaborates the concepts in simple and explanatory ways. At the same

time these contents cannot be considered as base to arrive at the conclusion of framing

research objectives as it is not supported through proper review by various researchers

working in the area. Review of a scientific research paper is a tedious work. It needs the

prior knowledge of the area of research. The scientific research papers are highly

structured, compact and precise in explanation. The researchers need to adopt a certain

path for doing literature review of such literature. There has been many procedures and

processes defined by the researchers to undergo through and arrive at certain conclusions

of research objectives. The five stages of the review process adopted are discussed in

this chapter. In the literature survey, 31 papers of journals and conferences ranging from

the year 1998 to 2015 and have been reviewed. The categorical review related to the

image segmentation and brain tumor detection has been done. Further including the

gaps in the published research, issue wise solution approaches with common finding,

strengths, weaknesses problem statement and objectives are also presented.

2.1 Review Process Adopted

The process diagram is shown in Fig.2.1, which includes in all five stages. The review

process is divided into five stages in order to make the process simple and adaptable by

every researcher. As it reflects from the literature that while beginning the finding of

research objectives, it is necessary to start with a broader domain of any area and

subarea of interest and narrow down to the specific issue, the process described in the

diagram includes the narrowing down along with the research objectives as outcome

with justification of the problem.

2.1.1 Stages of Review Process

Stage 0: Get the Feel.

Stage 1: Get Big Picture.



Stage 2: Get the Details.

Stage 3: Evaluate the Details.

Stage 3+:Synthesize the Details.

Fig. 2.1 Review Process Stage Analysis

The details of various stages followed are discussed below.

2.1.1 Stage 0: Get a

This stage is the beginning of the literature review process where in one has to broadly

select the area of interest and start searching the scientific research papers from valid

sources. More than 70 papers have been passed through this process. The whole

dissertation concept is related only on the image segmentation, image enhancement and

brain tumor detection and the algorithms that were developed on brain tumor

segmentation.

2.1.3 Stage 1:Get the

In order to understand the paper broadly and get an idea whether the paper exactly

belongs to the research area or subareas elected or it deviates, if deviates how much,

these concepts are made clear this stage, known as Get Big Picture. In this stage the

author knowing about the problem which was the author's attempt to solve. The solution

value and the knowing result are telling about the criteria of the technique was having. In

that step the various types of brain tumor detection techniques, image enhancement and

image segmentation are discussed. After finding the all solution approaches it was found

that the speed of computation is no longer an issue for researchers and the focus is

directed towards improvement of information from image obtained through the accurate

picture of brain tumor. Total 33 research papers could be selected through applying this



process on more than 70 papers.

2.1.3Stage2: Get the

Stage2 deals with going in depth of each research paper and understand the details of the

methodology used, problem justification to significance & novelty of the

problem/solution approach exist, precise question addressed, key contribution, scope &

limitations of the work presented.

After reading all the paper related to the tumor detection, image segmentation it was

found that the many papers are totally focused on the noise reduction technique, some

papers are related to segmentation to get an accurate picture, automation of brain tumor,

computation process, decision making technique. By using accurate diagnosis of brain

tumor, proper brain segmentation is required to be used to carry out an improved

diagnosis and treatment.

2.1.4

This stage provides an insight how to deal with evaluation of the details presented by

the researchers. In the beginning, it is not possible to evaluate these details, as it needs

to check in relative and comparative aspects in the domain of topic. This stage

evaluates the details in relation to the significance of the problem, novelty of the

problem, significance of the solution, novelty in approach, validity of claims, etc.

Here the detail about the detecting brain tumor through Magnetic Resonance Images

has been explored and comparative study has been carried out.

2.1.5 Stage 3+: the

Stage3+ deals deals with synthesis of the data, concept & the results presented by the

authors. This stage does not only require the understanding of all research work related

to the topic, but also requires creative thinking and good knowledge in the subarea of

research. Here all possible situations in general needed to be considered while

generalizing.

The brain tumor detection required image segmentation through MRI, to produce

images of soft tissues of human body. For this purpose, the brain is partitioned into two

distinct regions. This is considered to be the most important but difficult part of the

process of detecting brain tumors. A variety of algorithms were developed for

segmentation of MRI images by using different tools and techniques. However here



presents the detection through the entropy measures and then the analysis on the basis

that which will provide the best result.

2.2 An efficient Brain Tumor extraction from MRI Images using Entropy Measures

After an exhaustive literature review, there have been different types of approaches for

solving the power problems in the circuit. Total 53 papers were reviewed belonging to

conference, journal and transaction publications ranging from year 2001 to 2014. All the

papers were related to single issue of Brain Tumor Detection.

Table 2.2 Categorical Review of Research Papers

S.NO. Name of Issue Total Paper

Reviewed

Number of Paper Reviewed

Conf. Journal Transaction

1 Image restoration 8 8 - -

2 Enhancement of

image

13 13 - -

3 Brain Tumor

detection from MRI

image

32 26 1 5

An efficient Brain Tumor extraction from MRI

Images using Entropy Measures

Most of the papers were centered on a particular issue that was preserving the original

colors indifferent segments of the original image. The approach for color image

segmentation, which approximately preserves the colors in different segments, was

presented through various mechanisms for different entropy measures. The summaries

of the review are shown below.

[M. Stella Atkins, et-al, 1998] has developed a robust fully automatic method for

segmenting the brain from head magnetic resonance (MR) images, which works even in

the presence of radio frequency (RF) inhomogeneities. This method used an integrated

approach which employs image processing techniques based on anisotropic filters and

contouring techniques. The algorithm consists of three incremental steps. The first step

used histogram analysis to localize the head, providing a region which must completely

surround the brain. The second step used nonlinear anisotropic diffusion and automatic

thresholding to create a mask that isolates the brain within the detected head region.



Using the mask as a seed, the final step employs an active contour model algorithm to

detect the intracranial boundary. It was the multistage process, involving first removal

of the background noise leaving a head mask, then finding a rough outline of the brain,

then refinement of the rough brain outline to a final mask [4].

[C.C. Leung, et-al, 2003] has proposed a new approach to detect the boundary of brain

tumor based on the generalized fuzzy operator (GFO). The GFO map all the pixels in

the original image and all the values were passed to the new fuzzy set. The non-

homogeneities density tissue of the brain with tumor could result in achieving the

inaccurate location in any boundary detection algorithms. In MRI images the Boundary

detection with brain tumor was an important image processing technique. It was a

simple but effective method using in boundary detection. The special properties of this

GFO searched the boundary in high accuracy and obtained the fine edge based on its

generalized fuzzy set. The fuzzy sets separate the edge, normal tissue and tumor

respectively [16].

[M. Sasikalal, et-al, 2005] has proposed a computer based system for the detection of

glioblastoma multiform in brain images and compared feature selection algorithms for

the detection of glioblastoma multiform in brain images. Texture features were

extracted from normal and tumor regions (ROI) using spatial gray level dependence

method and wavelet transform. A very difficult problem in classification techniques was

the choice of features to distinguish between classes. The feature optimization problem

was addressed using a Genetic Algorithm (GA) as a search method. Principal

component analysis, classical sequential methods and floating search algorithm were

compared against the genetic approach in terms of the best recognition rate achieved.

The normal and tumor images were classified with an accuracy of97.3% using the entire

feature set [22].

[Phooi Yee Lau, et-al, 2005] has proposed an analytical method to detect tumors in

digitized medical images for 3D visualization. The EGH parameters were used in a

supervised block of input images. These feature blocks were compared with

standardized parameters (derived from normal template block) to detect abnormal

occurrences, e.g. image block which contain lesions or tumor cells. The abnormal

blocks were transformed into three-dimension space for visualization and studies of

robustness. Experiments were performed on different brain disease based on single and



multiple slices of the MRI dataset. The method was effectively capable of identifying

tumor areas in T2-weighted medical brain images taken under different clinical

circumstances and technical conditions, which were able to show a high deviation that

clearly indicated abnormalities in areas with brain disease [19].

[Satish Chandra, et-al, 2009] has proposed a modified version of PSO based algorithm

for the classification of MRI images. The algorithm finds the centroids of number of

clusters, where each cluster groups together brain tumor patterns, obtained from MRI

Images. The results obtained for three performance measures were compared with those

obtained from Support Vector Machine (SVM). The performance analysis shows that

qualitative results obtained from the proposed model were comparable with those

obtained by SVM. However, in order to obtain better results from the proposed

algorithm it was needed to carefully select the different values of PSO control

parameters [5].

[Tao Wang, et-al, 2009] has proposed a new approach called the fluid vector flow

(FVF) active contour model to address problems of insufficient capture range and poor

convergence for concavities. With the ability to capture a large range and extract

concave shapes, FVF demonstrates improvements over techniques like gradient vector

flow, boundary vector flow, and magnetostatic active contour on three sets of

experiments: synthetic images, pediatric head MRI Images, and brain tumor MRI

images from the internet brain segmentation repository. Experiment on synthetic images

and head MRI images shown that FVF approach had produced better results.

Quantitative experiments on brain tumor images presented that FVF has the largest

mean (0.61) and median (0.60) with smallest standard deviation (0.05) using TM.

Mixed effects model with random data and test effects was used to statistically compare

the differences between FVF and other methods [27].

[M. Murugesan, et-al, 2009] has proposed an automated system for efficient detection

of brain tumor in EEG signals using Artificial Neural Networks (ANNs). The proposed

system had taken an EEG signal with artifacts as input. Firstly, the inputted EEG signal

was subjected to artifacts removal by means of wiener filtering. Then, features of

interest for brain tumor detection were extracted from the EEG signal. EEG signal were

extracted using spectral estimation. Specifically, spectral analysis was achieved by

using Fast Fourier Transform that extracted the signal features buried in a wide band of



noise. The clean EEG data thus obtained was used as training input to the feed forward

back propagation neural network. The trained feed forward back propagation neural

network when fed with a test EEG signal, effectively detected the presence of brain

tumor in the EEG signal. The experimental results demonstrated the effectiveness of the

proposed system in artifacts removal and brain tumor detection [17].

[Ahmed Kharrat, et-al, 2009] has proposed an efficient detection of brain tumor based

on mathematical morphology, wavelet transform and K-means technique. The method is

composed of three steps: enhancement, segmentation and classification. The algorithm

reduces the extraction steps through enhancing the contrast in tumor image by

processing the mathematical morphology. The segmentation and the localization of

suspicious regions were performed by applying the wavelet transforms. To improve the

quality of images and limit the risk of distinct regions fusion in the segmentation phase

an enhancement process was applied. A mathematical morphology was adopted to

increase the contrast in MRI images. Then Wavelet Transform in the segmentation

process was applied to decompose MRI images. At last, the k-means algorithm was

implemented to extract the suspicious regions or tumors [13].

[T.Logeswari, et-al, 2010] has proposed a segmentation method for detection of brain

tumor consisting of two phases. In the first phase, the MRI brain image was acquired

from patients database, In that film artifact and noise were removed. After that

Hierarchical Self Organizing Map (HSOM) was applied for image segmentation. The

HSOM was the extension of the conventional self-organizing map used to classify the

image row by row. In this lowest level of weight vector, a higher value of tumor pixels,

computation speed was achieved by the HSOM with vector quantization [15].

[Somojit Saha, et-al, 2010] has proposed an automated segmentation algorithm of the

head contour and the entire brain. The approach used a high resolution MR image

acquisition protocol for better visualization of normal gray structures of brain. Using

signal nulling effect the author has emphasized enhancing the intensity difference

between white and gray matters in the reconstructed MR image. This leads to the

generation of a histogram where pixel values of different anatomical structures were

distributed around separate dominant modes. The algorithm has been tested on 24

dataset of coronal view with great success and validated by a robust index with highly

encouraging outcome. For validation study, some slices were selected such that the



entire range of the image volume from anterior to posterior was covered. A domain

expert traced the target manually on each slice and it was compared with the

automatically drawn head and brain contour using the similarity index [21].

[D.K. Somwanshi, et-al, 2011] has proposed a method that provides complete

diagnosis of the internal structure of the body and even detects the smallest

abnormalities through MRI scanned images. The method introduced was the image

processing based method that performed the texture feature of MRI image and

segmentation has been done. Texture analysis of the 5 different images of each case is

done by finding their contrast, correlation, energy, homogeneity and entropy, and then

their range is obtained. In order to detect the presence of the abnormality in the image,

its texture analysis of both the normal and abnormal images were done. On the basis of

the values of abnormal image, the range was calculated and further the texture feature of

normal image was compared and the feature lying outside the range finally detects the

abnormality in the biomedical image [26].

[V. Salai Selvam, et-al, 2011] has proposed a Scalp EEG with modified Wavelet-ICA

and Multi-Layer Feed Forward Neural Network for Brain Tumor Detection. Use of

scalp EEG for the diagnosis of various cerebral disorders is progressively increased.

The chosen EEG epoch belonged to a brain tumor case was considered as positive and

that it belonged to a normal case as negative. Then the four possibilities of the network

outcomes were True Positive (TP) if the network decided that a chosen EEG belonged

to a brain tumor case when it actually did, True Negative (TN) if the network decided

that a chosen EEG belonged to a normal case when it actually did, False Positive (FP) if

the network decided that a chosen EEG belonged to a brain tumor case when it actually

belonged to a normal case and False Negative (FN) if the network decided that a chosen

EEG belonged to a normal case when it actually belonged to a brain tumor case. From

the ROC it was clear that the performance of the proposed method in detecting the brain

tumor using the scalp EEG was very much encouraging [22].

[V.P. Gladis, et-al, 2011] has proposed method to segment a human brain of MRI

image in which tumor detection and characterization were considered using HSOM and

Wavelet packets feature spaces. In the first phase, the MRI brain image was acquired

from patients database, In that film artifact and noise were removed, and Hierarchical

Self Organizing Map (HSOM) was applied for image segmentation. The HSOM was the



extension of the conventional self-organizing map used to classify the image row by

row. In the second phase, the feature of the MRI image was extracted first. Therefore,

the proposed method has the potential to non-invasively determine the type of the brain

tumor [9].

[M. Usman Akram, et-al, 2011] has proposed a method for automatic brain tumor

diagnostic system from MRI images. The system consists of three stages to detect and

segment a brain tumor. In the first stage, MRI image of brain was acquired and

preprocessing was done to remove the noise and to sharpen the image. In the second

stage, global threshold segmentation was done on the sharpened image to segment the

brain tumor. In the third stage, the segmented image was post processed by

morphological operations and tumor masking in order to remove the false segmented

pixels. The proposed method enhanced the MRI image and segments the tumor using

global thresholding. False segmented pixels were removed using morphological

operations. The proposed method was invariant in terms of size, shape and intensity of

brain tumor. Experimental results show that the method performed provided the 92%

accuracy in enhancing, segmenting and extracting the brain tumor from MRI images

[14].

[Wang Yang, et-al, 2011] has proposed a Contour-based and Region Based methods

for segmenting brain tumors in 3D magnetic resonance images. This method was

applicable to different types of tumor. It takes the advantage of Fuzzy Classification for

automating the algorithm and the good quality segmentation result of deformable

models to improve the segmentation. This was achieved by IKFCM classification

method, morphological operation and a parametric 3D deformable model First the brain

was segmented using a new approach, robust to the presence of Tumors. Then a Tumor

detection was performed, based on improved Fuzzy classification. Applications on

several data sets with different Tumor sizes and different locations show that the

method works automatically with high quality of segmentation and were robust to inter-

individual variability for all types of fully enhancing Tumors [29].

[K. S. Angel Viji, et-al, 2011] has developed a brain tumor segmentation method and

validated using MRI Data. In Preprocessing and Enhancement stage, medical image was

converted into standard formatted image. Segmentation subdivides an image into its

constituent regions or objects. This method can segment a tumor provided that the



desired parameters are set properly. The author focuses on the computation of steepest

slope lines, and produces zero width watersheds. Also, this method has a high degree of

locality that it was suitable for parallel implementation. In this, after a manual

segmentation procedure the tumor identification, the investigations has been made for

the potential use of MRI data for improving brain tumor shape approximation and 2D &

3D visualization for surgical planning and assessing tumor [18].

[K.K. Sharma, et-al, 2012] has proposed a threshold selection technique for color

image segmentation problem, which approximately preserves the colors in different

segments. The threshold values obtained are dependent on the particular definition of

the entropy chosen, which in turn affects the segmentation results. In the proposed

approach, threshold selection in each of the three component (RGB) images is done on

the basis of different entropy measures. It was further observed that the segmentation

results obtained using Havrda-Charvat entropy measures were better than other entropy

measures in the sense of preservation of colors in different segments [12].

[Rupsa Bhattacharjee, et-al, 2012]has proposed a novel algorithm to feature out tumor

from diseased brain Magnetic Resonance (MR) images. In this work, based on a study

of quality parameter comparison of two filters, adaptive median filter was selected for

de-noising the images. Image slicing and identification of significant planes were done.

Logical operations were applied on selected slices to obtain the processed image

showing the tumor region. A novel image reconstruction algorithm was developed

based on the application of Principal Components Analysis (PCA). This reconstruction

algorithm was applied on original raw images as well as on the processed images.

Results of this work confirm the sole efficiency of the developed image processing

algorithm to detect brain tumor [9].

[Azian Azamimi Abdullah , et-al, 2012] has proposed a brain tumor detection method

based on cellular neural networks (CNNs). CNN was widely used for image processing,

pattern, target classification, motion detection and signal processing. To examine the

location of tumor in the brain, Magnetic Resonance Imaging (MRI) was used.

Radiologists evaluated the grey scale MRI images. This procedure was really time and

energy consuming. To overcome this problem, an automated detection method for brain

tumor using CNN was developed. The output of the images was the image segmentation

based on black and white. Black represented as normal area and the white area



represented as tumor area. By using the template in the CNN simulator, output of the

desired image could be performed. Therefore, many templates were combined in order

to obtain an accurate result that will help radiologists detecting the tumor in brain

images easily [3].

[Sahar Ghanavati, et-al, 2012] has proposed an automatic tumor detection algorithm

using multi-modal MRI. A multi-modality framework for automatic tumor detection

was presented, fusing different Magnetic Resonance Imaging modalities including T1-

weighted, T2-weighted and T1 with gadolinium contrast agent. The intensity, shape

deformation, symmetry, and texture features were extracted from each image. The

AdaBoost classifier was used to select the most discriminative features and to segment

the tumor region. Multi-modal MRI Images with simulated tumor was used as the

ground truth for training and validation of the detection method. The results on

simulated and patient MRI show 100% successful Tumor detection with average

accuracy of 90.11% [10].

[Sarah Parisot, et-al, 2012] has proposed a novel approach for detection segmentation

and characterization of brain tumors. In this the author combines an image-based

detections schema with identification of the tumors corresponding preferential location,

which was associated to a specific spatial behavior. This method exploits prior

knowledge in the form of a sparse graph representing the expected spatial positions of

tumor classes. Such information was coupled with image-based classification

techniques along with spatial smoothness constraints towards producing a reliable

detection map within a unified graphical model formulation. Towards optimal use of

prior knowledge, a two-layer interconnected graph was considered with one layer

corresponding to the low-grade glioma type (characterization) and the second layer to

voxel-based decisions of tumor presence. Efficient linear programming both in terms of

performance as well as in terms of computational load was considered to recover the

lowest potential of the objective function. The outcome of the method refers to both

tumor segmentation as well as their characterization [16].

[Yao Tien Chen, et-al, 2012] has proposed an approach integrating 3D Bayesian level

set method with volume rendering for brain tumor and tissue segmentation and

rendering. A prior probability estimation of the tumor and tissue was incorporated into

3D Bayesian level set method for 3D segmentation. The 3D Bayesian level set method



was then used to continuously segment the 3D targets from a series of brain images. To

facilitate 3D volume visualization of medical-image dataset, ray casting was conducted

to render the targets and construct the surface of the targets. Experiment results were

finally reported in terms of segmenting, rendering, and surface reconstructing of the

tumor, tissue, and whole brain [12].

[Natarajan P, et-al, 2012] has proposed a morphological processing technique that has

proved miraculously helpful in various image extraction and filtering techniques. The

morphological operators changed the structuring elements of the image according to

their use. Some operators like open, spur, dilate and close had proved helpful in

extracting the brain tumor from the MRI brain images. Pre-processing of the MRI was

done using gray scaling, histogram equalization and filtering techniques. Threshold

segmentation was used to work on the desired region of the image. Thus by applying the

image subtraction could get the final brain tumor image [13].

[Vijayshree Gautam, et-al, 2013] has proposed an approach for threshold selection

purpose in color image segmentation problems. The threshold values obtained were

dependent on the particular definition of the entropy chosen, which in turn affects the

segmentation results. It was further observed that the segmentation results obtained

using Havrda-Charvat entropy measures are better than other entropy measures in the

sense of preservation of colors in different segments. Simulation results performed in

MATLAB. The entropy function versus individual RGB component level plot is

obtained for Shannon and non-Shannon entropy measures. A typical plot for entropy

function using Havrda-Charvat entropy measure was done [18].

[Pavel Dvorak, et-al, 2013] has proposed a method for the detection of images

containing an abnormality caused by tumor. The goal was to determine whether the

MRI Image of a brain contains a tumor. The proposed method works with T2-weighted

magnetic resonance images, where the head was vertically aligned. The detection was

based on checking the left-right symmetry of the brain, which was the assumption for

healthy brain. The algorithm was tested by five-fold cross validation technique on 72

images of brain containing Tumors and 131 images of healthy brain. The proposed

method reached the true positive rate of 91.16% [7].

[T.Rajesh, et-al, 2013] has proposed an automated system for classification of MRI

brain images with different pathological condition. Many cancer forms can only be



diagnosed after a sample of suspicious tissue has been removed and tested. Pathologists

view pathologic tissues, typical1y with microscopes, to determine the degree of

normalcy versus disease. This process was time consuming and fatiguing. The system

described here classified the abnormality into benign or malignant in an automated

fashion. The author used conceptual1y simple classification method using Feed Forward

Neural Network. Texture features were calculated using Rough set theory. The

proposed system effectively classified the abnormality of brain tumors [15].

[Atiq Islam, et-al, 2013] has proposed a novel multifractal (multi-FD) feature

extraction and supervised classification techniques for improved brain tumor detection

and segmentation. The multi-FD feature characterizes intricate tumor tissue texture in

brain MRI as a spatially varying multifractal process in brain MRI. On the other hand,

the proposed modified AdaBoost algorithm considers wide variability in texture features

across hundreds of multiple-patient MRI slices for improved tumor and non-tumor

tissue classification. Experimental results with 14 patients involving 309 MRI slices

confirm the efficacy of novel multi-FD feature and modified AdaBoost classifier for

automatic patient independent tumor segmentation. In addition, comparison with other

state-of-the-art brain tumor segmentation techniques with publicly available low-grade

glioma inBRATS2012 dataset presented that the method outperform other methods for

most of the patients. The computation complexity of multi-FD feature was linear and

increased with slice resolution (number of pixel), block size, and the number of wavelet

levels [11].

[J.Vijay, et-al, 2013] has proposed a combine method of segmentation and K-means

clustering that described an efficient method for automatic brain tumor segmentation for

the extraction of tumor tissues from MRI Images. The main argument of the proposed

modifications is on the reduction of intensive distance computation that takes place at

each run (iteration) of K-means algorithm between each data point and all cluster

centers. In this method segmentation was carried out using K-means clustering

algorithm for better performance. This enhances the tumor boundaries more and was

very fast when compared to many other clustering algorithms. The proposed work also

reduces the computational complexity and also provides an accurate method of

extracting the Region of Interest (ROI).The proposed technique produced appreciative

results [27].



[Woo Kyung Moon, et-al, 2013] has proposed a computer-aided detection (CAD)

system based on multi-scale blob detection for analyzing ABUS images. After speckle

noise reduction, Hessian analysis with multi-scale blob detection was adopted to detect

the lesions by using blobness measurements of ABUS images. Tumor candidate

selection was then applied to remove the redundant non-tumors from the tumor

candidates. The tumor likelihoods of the remaining tumor candidates were estimated

using a logistic regression model with blobness, internal echo, and morphology features.

Finally, the tumor candidates with tumor likelihoods higher than a specific threshold

were considered to be tumors [7].

[Solmaz Abbasi, et-al, 2014] has proposed a method for 3D medical image

segmentation to detect brain tumor in MRI images by combining Clustering and

Classification methods to decrease the complexity of time and memory. The author

presented a hybrid method in which clustering(for obtaining region of interest) and

classification (for region of interest- segmentation) were used. In the first phase, non-

negative matrix factorization with sparseness constraint method was used to separate the

region of interest from the image. In the second phase, the classification of the region of

interest was performed. This method had achieved a fast speed for segmentation of MRI

3D images and evaluated with criteria of Dice's and Jacquard's coefficient on the brain

tumor from magnetic resonance image obtained from the Brats2013 database [2].

[Akram Rashid, et-al, 2014] has proposed a method from Electroencephalogram that

eliminate the noise of power line, noise of human body muscles, noise of human lungs

and noise of the base line. In this the basic important algorithms used were Kalman

filter. The Electroencephalogram signals were highly contaminated with various

artifacts both from subject and from equipment interferences. For efficient detection of

tumor artifacts exist in the electroencephalogram signal were removed using analogue

filtering. After than the Fast Independent Component Analysis algorithm was used to

separate the noise and get the features which were buried in the extended band of noise.

For problem solution a unique Fast Independent Component Analysis filter was being

proposed [12].

[Parveen, et-al, 2015] has proposed a new hybrid technique based on the support vector

machine (SVM) and fuzzy c-means for brain tumor classification. In this technique the

image was enhanced using enhancement technique such as contrast improvement, and



mid-range stretch. The brain MRI images were classified using SVM techniques which

were widely used for data analyzing and pattern recognizing. It creates a hyper plane in

between data sets to indicate which class it belongs to. Double thresholding and

morphological operations were used for skull striping. Fuzzy c-means (FCM) clustering

was used for the segmentation of the image to detect the suspicious region in brain MRI

image. The main objective of this work was to develop a hybrid technique, which can

classify the brain MRI images successfully and efficiently via Fuzzy C- means and

support vector machine (SVM) [18].

[ Padmakant Dhage, et-al, 2015 ] has developed a technique to solve the problem for

tumor case of clinical MRI analysis. The proposed technique was based on Watershed

segmentation and it has been successfully tested on MRI image data. Developed

algorithm was used to know about the location and size of the tumor. The author

illustrates the ability of watershed segmentation to separate the abnormal tissue from the

normal surrounding tissue to get a real identification of involved and noninvolved area

that help the surgeon to distinguish the involved area precisely. At the end of the

process tumor was extracted from the MRI Image and its exact position and shape was

determined and various parameters like perimeter, eccentricity, entropy and centroid

was calculated. All parameters which were extracted using developed algorithm specify

the size and other dimensions of the tumor [8].

2.2.2 Common Findings obtained in the issue Brain Tumor Detection from MRI

Images

In the contour deformable model with regional base technique, the performance

was insufficient to obtain the fine edge in the tumor.

The efficient system was presented to perform early detection of brain Tumors

from EEG Signals with the aid of Artificial Neural Networks.

The feed forward back propagation neural networks provide computationally

more efficient Detection Accuracy with 98.7654% once trained.

Feature selection algorithms were categorized into Exponential, Randomized

and Sequential algorithms to attain high accuracies.

The Genetic Algorithm based Global Search was used for selecting the best set

of features.



The classification of normal and tumor regions was done using texture features

from gray level dependence method (SGLDM) and wavelet features.

To improve the quality of images and limit the risk of distinct regions fusion in

the segmentation phase an enhancement process was applied on brain images to

increase the contrast in MRI images and to extract the suspicious regions or

tumors.

The segmentation and the localization of suspicious regions were performed by

applying the wavelet transforms.

The HSOM was the extension of the conventional self-organizing map used to

classify the image row by row.

A mathematical morphology was adopted to increase the contrast in MRI

images.

The Hybrid algorithm used Contour-based and Region based Methods to

segment Brain Tumors in 3D MRI Images.

The HSOM and Wavelet packets feature spaces method have the potential to

non-invasively determine the type of the brain tumor

Multi -modal MRI Images with simulated tumor were used as the ground truth

for training and validation of the detection method.

The performance of clustering algorithm for image segmentation was highly

sensitive to features that were used and the types of objects in the image and

hence generalization of this technique was difficult.

2.3 Various solution approaches in

from

The Conceptual explanation of various solution approach and algorithms used are

summarized in the table below. This section would include, the issue-wise solution

approaches, methodologies used by the researchers, with results obtained. Based on

Shannon Algorithm different authors have used different data that is presented below:



Table 2.3: Categorical Review of Research Paper

Author Solution

Approach

Input

Parameters

Accuracy

(%) Performance Parameters Results

[C.C. Leung, et-al, 2003]

Generalised

Fuzzy Operator

(GFO)

MRI Images 98

Comparison with CDM and modified GFO(mGFO)

Accurately search the boundary of the Brain Tumor.

Real edge CDM mGFO

Total no. of

pixels within

tumor

1749 2443 1783

Number of

mismatch pixels

out of tumor

1749 (1689)

+694 (41.09%) +34 (2.01%)

Number of

mismatch pixels

inner of tumor

1749

(60) -60 (100%) -4 (6.67%)

[Ahmed Kharrat, et-

al, 2009]

Mathematic

Morphology MRI Images 87

MRI Image CLAHE Beghadi Mathematic

Morphology The extraction of the tumor was accomplished

manually from segmentation result and it was

observed that Mathematical Morphology was better. Abnormal Image 0.8172 0.9997 1.5073

[M. Stella Atkins,et-al,1998]

Automatic intracranial

boundary

detection algorithm

MRI data sets

96

Comparision of Manual and Automatic Brain Segmentation

The algorithm has proven effective on clinical and

research MRI data sets acquired from several

different scanners

Slice No. Manual Area Auto Area Similarity

Index

4 8912 10269 0.925

7 20264 22472 0.929

8 22035 23918 0.954

23 20109 20341 0.980

24 15909 17201 0.958

[PadmakantDhage,

et-al, 2015]

Watershed

Segmentation

MRI Images

97.3

S.No. Parameter Median Filter Bilateral Filter

Accurately remove the noise from MRI Images

without disturbing the edges.

1 MSE 2.98 4.05

2 PSNR 43.3 42.0

3 Contrast 0.10 0.01

4 Corelation 0.99 0.99

[SolmazAbbasi, et-al,

2014]

Combined Clustering and

classification

Mechanism

3D Medical

Image

N/A

Dice

Complte

Tumor

Dice

Tumor

Core

Dice

Enhancing

Tumor

Jaccard

Complete

tumor

Jaccard

Tumor

Core

Jaccard

Enhancing

Tumor This method decreases the computational complexity in

time and memory. 0.8497 0.7902 0.7941 0.7467 0.6678 0.6884

[AzianAzamimi

Abdullah, et-al,

2012]

Cellular Neural

Networks

(CNN)

MRI Images N/A N/A

The output of the image detected in shorter time and based

on black and white. Black portion represented as normal

area and white are represented as tumor area.

[SaharGhanavati, et-

al, 2012]

Multi -Modality

Framework

T1 and T2

weighted Magnetic

Resonance

Imaging

90.11

Cases Accuracy Specificity

The better segmentation at the edges of tumor was obtained. Case1 90.11% 92.26%

Case2 89.12% 91.19%

[Wang Yang, et-

al,2011]

Hybrid

Algorithm

3D

Magnetic

True Detection

Tp = NTP/NM*100%

The average computation time for detection of tumor was

reduced.



(Contour based

and region based)

Resonance

Images

N/A False Detection

Fp=NFP/NA*100%

Tumor Type Method TP %

FP %

FE1 FPCM 82 1.92

FE1 IKFCM 93 7.63

FE2 FPCM 93 9.57

FE2 IKFCM 98 4.40

[Satish Chandra, et-

al,2009]

Clustering

Algorithm

based on

PSO

110

abnormal and 62

normal axial

MRI images

92.41

Results for various Algorithm

Performance is improved in terms of execution time and

tumor pixel detected.

Classifier Precision Recall Accuracy

Proposed PSO

Based algorithm 92.76 96.24 94.42

SVM (Polynomial

kernel) 93.33 95.28 92.71

AdaBoost 90.25 91.66 89.31

[T.Logeswari, et-al,

2010]

Hierarchical

Self

Organizing Map

MRI Data N/A

neighborhood

pixel

Winning

neuron

Number

of seg

pixel

Exe-time weight

The target area is

segmented and Brain Tumor detection done accurately 3x3 209 795 13.76 14

5x5 201 1073 14.96 8

7x7 194 1285 15.20 15

9x9 186 1594 11.05 23

11x11 177 1881 11.53 32

[M. Murugesan, et-

al, 2009]

Artificial Neural

Network

325 samples of EEG

data.

98.7654 325 Samples of EEG

Data

Detection Accuracy The trained Feed forward back propagation neural network

has detected the presence of brain tumor in the test signal. Normal 94.4785

Abnormal 98.7654

[V. SalaiSelvam, et-

al, 2011]

Scalp EEG

withModified Wavelet-ICA

MRI and

CT-SCAN Data

N/A

Sensitivity or TPR 0.930

Improved the detection rate of the Tumor.

FPR 0.106

Accuracy 0.918

Specificity or TNR 0.894

[RupsaBhattacharjee,

et-al, 2012]

PCA Based

Reconstruction MRI Images N/A

Noise Details Filters Comparision

Abnormal area of the brain is detected and pixel wise

intensity is calculated.

Parameter Bilaterl Filter Adaptive

Noise Density

MSE 8242.5 1590.2

PSNR 8.9702 16.1163

UIQI 0.0742 0.4306

Guassian

Noise

MSE 1271.4 424.1774

PSNR 17.0879 21.8553

UIQI 0.1894 0.2520

[M. Usman Akram, et-al, 2011]

Global thresholding

100 MRI

Images

97

Parameter Value

The proposed method performs well in enhancing,

segmenting and extracting the brain tumor from MR images.

Standard Deviation 0.0013

Accurately Segmented 97%

Poorly Segmented 3%



[Woo Kyung Moon, et-al, 2013]

Multi -Scale

Blob Detection

Algorithm

ABUS image

97

Feature Set False Positive FOM

Successfully detected the abnormalities with tumor region.

Blobness 113.26 0.28

Internal Echo 42.04 0.23

Morphology 65.16 0.16

Blobness and

Internal Echo 39.81 0.33

Blobness and


Internal Echo and


All features with

IOF-CV 17.33 0.47

All features with

LOO-CV 17.45 0.47

[Natarajan P, et-al, 2012]

Threshold operation

MRI Brain Images

N/A

RGB 1. For black

R = G = B = 00000000

2. For white R = G =B = 11111111

Tumor is identified from the MRI Image accurately.

[T.Rajesh, et-al,

2013]

Feed Forward

Neural Network

MRI Brain

Images 90

Evaluation

Metrices Training Dataset

Proposed Testing

Dataset

Suspicious tissues are tested and many cancer forms are

diagnosed.

(TN)True Negative 9 10

(FP)False Positive 1 0

(TP)True Positive 10 8

(FN)False Negative 0 1

Specificity 0.9 1

Sensitivity 1 0.88

Accuracy 0.95 0.9

[M. Sasikala, et-al, 2005]

Artificial

Neural

Network

19 scans of

glioblastoma multiforme

and 18 scans

containing normal brain

images

97.3%

Feature Selection

Algorithm Feature Set

Classifier

Accuracy

The normal and tumor images are classified with an accuracy of 97.3%

SFS ASM, IDM, SVAR,

SENT 97.3%

SBS SVAR,ENT,

ENERGY, MEAN 97.3%

Plus 1- take away r

method

ASM, SVAR, IMC,

ENERGY, 97.3%

SFFS ASM, SVAR, SENT,

DENT 97.3%



Table 2.4: Comparison Table of different Entropy

Author Solution

Approach

Data used Performance Measures

(Threshold Value) Results

[Shruti

Mathur, et-al,

2014]

Shannon and non-

Shannon Entropy

jpg image

Entropy Kaju.jpg Algae.jpg America.jpg Havrda-Charvat entropy based image

segmentation technique is better for gray

images than any other entropy measures.

Shannon 10 83 197

Kapur 26 140 211

Vajda 5 141 209

Renyi 122 140 211

Havrda 83 113 161

[Vijayshree

Gautam, et-al,

2013]

Shannon and Non-

shannon Entropy

Color Image

Entropy Component Various individual RGB component

level plot is obtained for Shannon and

non-Shannon entropy measures.

R G B

Havrda-Charvat

Entropy

124 42 55

Shannon Entropy 17 39 106

Renyi Entropy 234 15 39

[Ahmad Adel

Abu Shareha,

et-al, 2008]

Renyis entropy Synthetic

Images Image No. Threshold using the

original Entropy

Threshold using

Textured Renyi

The experimental results show that the

proposed method enhances the

thresholding result and reduce the error

rate. 1 T=187 T=177

2 T=155 T=86

3 T=55 T=55

4 T=154 T=74

5 T=153 T=149

6 T=133 T= 133

7 T=178 T= 172

8 T=157 T= 154

[Siddheswar

Roy, et-al,

2000]

Shannon Entropy Three images

( balls,

molecule, rose)

Entropy

Function

Entropy

type

Balls Molecule Rose The experimental results was quite

satisfactory.

Shannon Global 369.7350 194.3193 180.9309

Shannon Local 296.1081 316.2784 204.4700

Shannon Conditional 227.3324 324.0741 219.7089

[K.K. Sharma,

et-al, 2012]

Shannon and Non-

Shannon Entropy

Ball Image Entropy

Component The segmentation results obtained using

Havrda-Charvat entropy measures were

better than any other entropy measures R G B

Kapur 20 23 71

Havrda-

Charvat 117 138 141

Shannon 64 80 198



Renyi 216 215 73

Vajda 10 21 72

[P.K. Saboo,

et-al, 2006]

Havrda-Charvat

Entropy

Tif images

Test Image T(0.3) The images were segmented perfectly

and a histogram was created using the

gray value.

Camaraman.tif 99

Kids.tif 32

Rice.tif 182

Tire.tif 111

Bonemarr.tif 122

Boats.tif 102

Bridge.tif 125

Lena.tif 122

[Amar Pratap

Singh, et-al,

2009]

Shannon and Non-

Shannon Entropy

Measures

Mammogram

Image Analysis

Society

Database

Images

Sample

S

R

HC

K

Results have demonstrated that Havrda

& Charvat entropy based feature for

classifying normal and abnormal

mammogram images. Mam1 4.76 6.92 8.79 7.99

Mam2 5.17 6.90 9.05 7.79

Mam3 5.24 7.01 9.34 7.88

Mam4 5.16 6.71 8.63 7.57

Mam5 4.84 6.78 8.51 7.81

Mam6 5.10 6.79 8.71 7.72

Baljit Singh

Khehra,et-

al,2011

Shannon and non-

shannon Entropy

mini-MIAS

database

(Mammogram

Image Analysis

Society

database

Test Image tS t

R t

HC t

k The result show that it is useful for

radiologists to find suspicious region

in mammogram.

mdb218 180 191 194 193

mdb236 186 200 201 201

mdb238 171 179 178 177

mdb219 185 194 198 198

mdb222 186 193 194 194

mdb227 184 193 194 194

mdb240 198 210 210 210

mdb245 177 198 200 199

mdb248 177 192 193 194

mdb253 191 203 205 205



2.4 Strengths and weaknesses of research found in the issue Brain Tumor

Extraction from MRI Images using Entropy Measure

After, the review of 32 research papers under the area of Brain Tumor Extraction from

MRI Images, I have been able to find, the strengths and weaknesses of various solution

approaches used to solve the issues discussed in previous chapters. This section would

enlist the strengths and weaknesses of the various methods and algorithms used.

2.4.1Strengths in the area of Brain Tumor Detection from MRI Images:

After, the review of thirty one research papers under the area of Brain Tumor

Extraction from MRI Images strengths and weaknesses of various solution approaches

used could be determined. This section mainly enlists the strengths of the various

methods and algorithms used.

The watershed transformation algorithm extracts shape and form related

information from images precisely [8].

The Vajda entropy measures provide the advantage of faster calculations over

Kapurs entropy measure [17].

Havrda-Charvat entropy based image segmentation technique was better for

gray images than any other entropy measures [12].

The major advantage of split and merge technique that it guaranteed connected

regions [21].

The Mean Shift algorithm provides a robust feature space analysis approach

which can be applied to discontinuity preserving smoothing and image

segmentation problems [6].

The Ncut method was applied directly to the image pixels, which were typically

of very large size [4].

The Ncut method was applied to perform globally optimized clustering and get

the final segmentation result [13].

The Mean Shift algorithm can significantly reduce the number of basic image

entities, and due to the good discontinuity preserving filtering characteristics, the

salient features of the overall image were retained [1].

Edge detection algorithm was used for object detection which serves various

applications like medical image processing, biometrics etc [23].



Thresholding was computationally inexpensive and fast, it was the oldest

segmentation method and was still widely used in simple applications [27].

Global thresholding method selects only one threshold value for the entire image

[3].

Edge preserving denoising was of great concern in medical images. Denoising

was performed to segmentize the image for better diagnosis [11].

2.4.2Weaknesses in the area of Brain Tumor Detection from MRI Images:

By applying the Ncut algorithm the performance and stability of the partitioning

highly depends on the choice of the parameters [19].

The drawbacks of the split and merge technique were, the results depend on the

position and orientation of the image, leads to blocky final segmentation and

regular division leads to over segmentation (more regions) by splitting [12].

If seeded region growing method was used then noise in the image can cause the

seeds to be poorly placed [8].

Local thresholding selects different threshold values for different regions [23].

The performance of clustering algorithm for image segmentation was highly

sensitive to features used and types of objects in the image and hence

generalization of this technique is difficult [15].

Clustering technique doesnt guarantee continuous areas in the image, even if it

does edges of these areas tend to be uneven, this was the major drawback [29].

2.5 Gaps in the published work

The Researcher had presented many techniques and methodologies to segment the MRI

images and to detect the Brain Tumor. All the Researchers had shown some

improvement with some gaps in their work. Those are:

A very few approaches had been made for Early Detection and classification of

Brain Tumors.

There are various approaches that have been proposed to deal with the task of

segmenting and detecting the brain tumors in MRI Images but the performance

of these approaches usually depends on the qualitative amount of information.



Most of the research work was directed towards the speed of computation that

was no longer an issue nowadays. The improvement of information obtained

from images and perfecting the process of segmentation to get an accurate

picture of the brain tumor must be given importance.

During the acquisition of medical images, there were possibilities that the

medical image one gets might be degraded. Very few works has been done in

order to reduce this problem.

2.5 Problem Statement

Brain Tumor was one of the frequent and leading causes of mortality, especially in

developed countries. Though brain tumor leads to death, early detection can increase the

survival rate. In this dissertation work the main emphasis laid on to design an approach,

which was a detection technique so that the system effectively detects and diagnose the

tumor in their early stage.

Thus the final title of the work chosen to achieve the main aim of early detection of

tumors in MRI Images is An efficient Brain Tumor Extraction from MRI Images

using Entropy Measures.

2.6 Final Objectives

1. To finalize the MRI images for experimentation.

2. To perform the global thresholding image Segmentation on MRI Images

3. To calculate the entropy and to check the threshold of each image in order to find

out the tumor affected region.

4. To compare and analyze the performance of the Shannon and non-Shannon entropy

measures.

So this chapter presents, Performance Evaluation Criteria used by various researchers,

details of Software used by researchers, Methodologies used by Researchers. Next

Chapter will provide the theoretical aspect of targeted work, details of Input/output

parameters that will be used during experimentations and details of softwares which

have been selected to carry out experimentation.



Chapter 3: Theoretical Aspects of Brain Tumor Extraction from MRI

Images

In previous chapter we have discussed literature review of different problems and

solutions in brain tumor detection. This dissertation focuses on accurate detection of

brain tumor, image segmentation, to minimize diagnostic errors, to improve the

accuracy by using Computer aided diagnostic. Different types of tumor detection

methods and some other techniques in order to get an accurate picture of brain tumor

from images obtained through the slice orientation and perfecting the process of

segmentation are discussed in this chapter.

In day-to-day life, new technologies are emerging in the field of Image processing,

especially in the domain of segmentation. Segmentation is the most important part in

image processing Fence off an entire image into several parts which is something more

meaningful and easier for further process. Segmentation may also depend on various

features that are contained in the image. It may be either color or texture. Before

denoising an image, it is segmented to recover the original image. The main motto of

segmentation is to reduce the information for easy analysis. Segmentation is also useful

in Image Analysis and Image Compression.

3.1 Classification of Image Segmentation

There are several existing techniques which are used for image segmentation. These all

techniques have their own importance.

Fig. 3.1 Image Segmentation Techniques

The popular techniques used for image segmentation are: thresholding method, edge

Image Segmentation

Methods

Threshold Methods

Edge Based Methods

Region Based Methods

Clustering Based Methods

Watershed Based Methods

PDE Based Methods

ANN Based Methods



detection based techniques, region based techniques, clustering based techniques,

watershed based techniques, partial differential equation based and artificial neural

network based techniques. These all techniques are different from each other with

respect to the method used by these for segmentation.

3.1.1 Thresholding Method

Thresholding methods are the simplest methods for image segmentation. These methods

divide the image pixels with respect to their intensity level. These methods are used

over images having lighter objects than background. The selection of these methods can

be manual or automatic i.e. can be based on prior knowledge or information of image

features. There are basically three types of thresholding.

1. Global Thresholding: This is done by using any appropriate threshold value/T. This

value of T will be constant for whole image.

2. Variable Thresholding: In this type of thresholding, the value of T can vary over the

image. This can further be of two types

Local Threshold: In this the value of T depends upon the neighborhood of x and y.

Adaptive Threshold: The value of T is a function of x and y.

3. Multiple Thresholding: In this type of thresholding, there

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