Technique Used for Cancer Detection

1. Technique Used for Cancer Detection

2. CONTENTS Introduction Texture Analysis based technique GLCM Example(Brain Tumour) Conclusion References

3. INTRODUCTION • Importance of Image processing In biomedical • Cancer is the leading cause of death

4. Introduction (Cancer Statistics) Cancer Scenario in India with Future Perspectives Research Article 2011

5. Texture Analysis based Technique Texture consists of texture primitives or elements called texels. What is texture?

6. Classification Of Texture • Based on attributes,Texture are of two types- • Microtextures • Macrotextures

7. 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?

9. 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. 0 1 0 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 0 0 1 0 d = (1, 1) 1 1 0 1 1 0 local texture patch displacement vector co-occurrence matrix Gray-level Co-occurrence Matrix • Example (2 gray levels): 2 9 10 4

13. Example… (4 gray levels)

14. Example… (8 gray levels)

15. 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.

16. Input Image

17. Matlab Code for Implementing GLCM 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',[])

18. GLCM Output glcm = 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)

20. Gray-level Co-occurrence Matrix

21. GLCM Features For Brain Tumor Classification • 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)

22. MRI Image

23. Data Sets(Cancer images) Class I (Astrocytoma)

24. Class II (Meningioma)

25. Class III (Metastatic bronchogenic carcinoma)

26. Class IV (Sarcoma)

27. Feature Extraction

28. Feature Extraction

29. 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.

30. References

31. Cont…

32. Cont…

33. Thank you!