1. Texture Classification Based on Co-occurrence Matrices Presentation III Pattern Recognition Mohammed Jirari Spring 2003

2. Co-occurrence Matrices The joint probability of occurrence of grey level a and b for two pixels with a defined spatial relationship in an image. The spatial relationship is defined in terms of distance d and angle θ. From these matrices, a variety of features may be extracted.

3. Co-occurrence Matrices (cont.) In my project, the matrices are constructed at a distance of d=1 and for angles θ=0°, 45°, 90°, 135°. For each matrix, eight features are extracted.

4. Co-occurrence Matrices (cont.) Can be formally represented as follows:

5. Example A 4X4 image with 4 grey-levels

6. Features Used Energy or angular second moment: Entropy: Maximum Probability: Inverse Difference moment: κ=2, λ=1

7. Features Used (cont.) Contrast: Homogeneity: Inertia or variance:

8. Features Used (cont.) Correlation

9. Matlab Code Code to extract features from images Co-occurrence and features

10. Results Features for Calcification Features for Well-defined/Circumscribed masses Features for Well-defined/Circumscribed masses Features for Spiculated masses Features for other, ill-defined masses Features for Architectural distortion Features for Asymmetry Features for Normal mammogram

11. What next? I plan to use the calculated features as training sets for my neural network, reducing the training set size from 1024X1024 to 8X4 per image. Also, a fifth co-occurrence matrix will be constructed as the mean of all four directions. May or may not help !!!