Technique Used for Cancer Detection

CONTENTS Introduction

Texture Analysis based technique

GLCM

Example(Brain Tumour)

Conclusion

References

INTRODUCTION

• Importance of Image processing In biomedical

• Cancer is the leading cause of death

Introduction (Cancer Statistics)

Cancer Scenario in India with Future Perspectives Research Article 2011

Texture Analysis based Technique

Texture consists of texture primitives orelements called texels.

What is texture?

Classification Of Texture

• Based on attributes,Texture are of two types-

• Microtextures

• Macrotextures

What texture analysis is used for

• To segment an image into regions with the same texture, i.e. as a complement to grey level or color.

• To recognize or classify objects based on their texture

8

Texture

•The most fundamental question is: How can we “measure” texture, i.e., how can we quantitatively distinguish between different textures?

Typical application for Texture Analysis

Cells from a tumour with poor prognosis

Cells from a tumour with good prognosis

10

Gray-level Co-occurrence Matrix

• GLCM is the statistical method of examining the textures that considers the spatial relationship of the pixels.

• The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image

• The spatial relationship is defined as the pixel of interest and the pixel to its immediate right (not fixed)

11

Gray-level Co-occurrence Matrix

• Each element (a, b) in the resultant GLCM is simply the sum of the number of times that the pixel with value a occurred in the specified spatial relationship to a pixel with value b in the input image.

• A GLCM is a matrix where the number of rows and columns is equal to the number of gray levels

12

Gray-level Co-occurrence Matrix

• Example (2 gray levels):

011011010010011011010010011011010010

local texture patch

d = (1, 1)

displacement vector

co-occurrence matrix

0

1

0 1

2 910 4

Example… (4 gray levels)

Example… (8 gray levels)

Gray-level Co-occurrence Matrix

• The matrix element P(i, j | ∆ x, ∆ y) is the relative frequency separated by a pixel distance (∆ x, ∆ y) .

• Matrix element also represented as P( i, j | d, θ) which contain the second

order probability values for changes between gray level i and j at distance d a particular angle θ .

Input Image

Calculate the gray-level co-occurrence matrix for a grayscale image.

I = imread('circuit.tif');glcm = graycomatrix(I,'Offset',[0 1]);

Calculate the gray-level co-occurrence matrix and return the scaled version of the image, SI, used by graycomatrix to generate the GLCM.

I = [ 1 1 5 6 8 8; 2 3 5 7 0 2; 0 2 3 5 6 7];[glcm,SI] = graycomatrix(I,'NumLevels',9,'G',[])

Matlab Code for Implementing GLCM

GLCM Outputglcm =

0 0 2 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

SI =

2 2 6 7 9 9 3 4 6 8 1 3 1 3 4 6 7 8

19

Gray-level Co-occurrence Matrix

•For a given co-occurrence matrix P(a, b), we can compute the following important characteristics: (Haralick Features)

ba

baP,

2 ),(Energy

ba

baPbaP,

2 ),(log),(Entropy

),(maxyprobabilit Maximum,

baPba

ba

baPba,

1 2,usually ,),(||Contrast

20

Gray-level Co-occurrence Matrix

ba

x baPa ),(

ab

y baPb ),(

ba

xx baPa ),()( 2

ab

yy baPb ),()( 2

What is brain tumor? Considered Four data sets.

Class I(35 year old)

Class II(75 year old)

Class III(42 year old)

Class IV(22 year old)

GLCM Features For Brain Tumor Classification

MRI Image

Data Sets(Cancer images)Class I (Astrocytoma)

Class II (Meningioma)

Class III (Metastatic bronchogenic carcinoma)

Class IV (Sarcoma)

Feature ExtractionClass/

FeaturesClass I Class II Class III Class

IVAutocorrelation

0.56958 2.089459 2.189791 2.00143

Contrast 1.732217 0.241603 0.272438 0.176089

Correlation 0.624228 0.5699 0.526011 0.658647

Cluster Prom. 0.375112 1.915923 1.65596 1.742295

Cluster Shade

1.256381 0.65392 0.489271 0.675075

Dissimilarity 0.892752 0.229177 0.256782 0.167784

Energy 0.892037 0.360822 0.326417 0.416605

Class/Features

Class I Class II Class III Class IV

Entropy 0.55052 1.301338 1.362772 1.1614

Homogeneity 2.098217 0.887483 0.874218 0.917492

Max. Prob. 3.574402 0.53491 0.482899 0.588983

Sum of S.V. 1.083287 2.167098 2.283165 2.047427

Sum average 0.228808 2.778051 2.855607 2.706693

Sum variance 0.542568 3.637988 3.77016 3.677384

Sum entropy 0.996494 1.117896 1.154711 1.026883

Diff. variance 0.54334 0.241603 0.272438 0.176089

Diff. entropy 0.975889 0.559004 0.595968 0.464998

Feature Extraction

CONCLUSION• Gray-level Co-occurrence Matrix

can be well implemented in Matlab by using graycomatrix function.

• GLCM can used to detect all types of cancer.

ReferencesMethods Paper Author YearGLCM GLCM Textural

Features for Brain tumor Classification((International Journal)

Nitesh Zulphe and Vrushsen Pawar

May 2012

GLCM Image texture Feature Extraction Using GLCM Approach

P. Mohanaiah, P. Sathyanarayana, L. GuruKumar

May 2013

Texture Analysis based techniques(GLCM),OO Texture Analysis(Circle fitting Algorithm)

A Recent Survey on Colon Cancer Detection techniques(IEEE)

Saima rathore ,Mutawarra hussain, Ahmad Ali and Asifullah khan

May/June 2013

Principle Component Analysis(PCA)

Diagnosis of Human Bladder Cancer cells at different stages using multispectral Imaging microscopy(IEEE)

Ching-Te Huang,Yung Shung Chen, Chie, Tong kuo and Hsiang- Chen Wang

May/ June 2014

Methods Paper Author Year Gabor Filter within Gaussian Rules, FFT, Watershed Algorithm

Lung Cancer Detection using Image Processing Techniques (Journal)

Mokhled. S. AL-Taraneh

Jan-June 2012

Ostu’s method , Gradient vector Flow(GVF),Colour based Image Segmentation using K-means Clustering

Image Processing for Skin cancer Features Extraction (Journal)

Md. Amran Hossen Bhuiyan Ibrahim Azad, Md, Kamal Uddin

Feb- 2013

Cont…

Cont…Methods Paper Author Year7) GLCM Segmentation

Coupled Textual Feature classification for Lung tumor Prediction (journal)

S.K Vijai Anand Ieccct’10

8) GLCM Automatic Characterization of mases in mammograms (IEEE)

Chakraborty J,Midye

2013

Thank you!