technique used for cancer detection
DESCRIPTION
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). - PowerPoint PPT PresentationTRANSCRIPT
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
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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)
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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
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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
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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
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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!