1. Mammogram Analysis – Tumor classification - Geethapriya Raghavan

2. Background Mammogram – Mammogram – X-Ray image (of gray levels) of inner breast tissue to detect cancer X-Ray image (of gray levels) of inner breast tissue to detect cancer Shows the levels of contrast characterizing normal tissue and vessels Shows the levels of contrast characterizing normal tissue and vessels Issues – Issues – Detect abnormalities (tumors) Detect abnormalities (tumors) Diagnosis - Classify as benign or malignant Diagnosis - Classify as benign or malignant Remove noise Remove noise

3. Microcalcifications Mammograms obtained from MIAS database

4. Methods.. Non-linear classifiers preferred over linear classifiers given the randomness in occurrence of tumor cells Non-linear classifiers preferred over linear classifiers given the randomness in occurrence of tumor cells Contemporary methods - supervised learning problem (Wei et al., 2005) Contemporary methods - supervised learning problem (Wei et al., 2005) Support Vector Machines (SVM) (Vapnik et al., 1997) Support Vector Machines (SVM) (Vapnik et al., 1997) Kernel Fisher Discriminant (KFD) Kernel Fisher Discriminant (KFD) Relevance Vector Machines (RVM) Relevance Vector Machines (RVM)

5. Method I - SVM SVM was used by Chang et al., on US images SVM was used by Chang et al., on US images Texture feature – microcalcification area, contrast. Texture feature – microcalcification area, contrast. Software – SVM Light ( (http://svmlight.joachims.org/) Software – SVM Light ( (http://svmlight.joachims.org/) The best fitting hyperplane f(x) = w T. x + b forms the boundary The best fitting hyperplane f(x) = w T. x + b forms the boundary For non-linear SVM, the ‘x’ in the above equation is replaced by a nonlinear function of ‘x’. For non-linear SVM, the ‘x’ in the above equation is replaced by a nonlinear function of ‘x’.

6. Method II Use of wavelet transform to decorrelate data (image) (Borges et al., 2001) Obtain wavelet coefficients as features Obtain wavelet coefficients as features Normalize coefficients and feed into Nearest Neighborhood classifier Normalize coefficients and feed into Nearest Neighborhood classifier Wavelet decomposition - Low frequency coefficients extracted at two levels and NNR run with euclidean distance as metric. Wavelet decomposition - Low frequency coefficients extracted at two levels and NNR run with euclidean distance as metric.

7. Results ClassifierMicrocalcification ContrastMicrocalcification Area Non-linear SVM67.7 %78 % Linear SVM42.8 %70.4 % NNR72 %76.2 %

8. Results - ROC Sensitivity = Number of True Positive Classifications Sensitivity = Number of True Positive Classifications Number of Malignant Lesions Number of Malignant Lesions Specificity = Number of True Negative Classifications Number of Benign Lesions Number of Benign Lesions Sensitivity (y) vs. Specificity (x) Dotted = lower bound Dotted = lower bound Red line = Wavelets + NNR Red line = Wavelets + NNR Black curve = linear SVM Black curve = linear SVM