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Mammogram Analysis – Tumor classification

Mammogram Analysis – Tumor classification. - Geethapriya Raghavan. Background. Mammogram – X-Ray image (of gray levels) of inner breast tissue to detect cancer Shows the levels of contrast characterizing normal tissue and vessels Issues – Detect abnormalities (tumors)

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Mammogram Analysis – Tumor classification

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  1. Mammogram Analysis – Tumor classification - Geethapriya Raghavan

  2. Background • Mammogram – • X-Ray image (of gray levels) of inner breast tissue to detect cancer • Shows the levels of contrast characterizing normal tissue and vessels • Issues – • Detect abnormalities (tumors) • Diagnosis - Classify as benign or malignant • 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 • Contemporary methods - supervised learning problem (Wei et al., 2005) • Support Vector Machines (SVM) (Vapnik et al., 1997) • Kernel Fisher Discriminant (KFD) • Relevance Vector Machines (RVM)

  5. Method I - SVM • SVM was used by Chang et al., on US images • Texture feature – microcalcification area, contrast. • Software – SVM Light ((http://svmlight.joachims.org/) • The best fitting hyperplane f(x) = wT . x + b forms the boundary • 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 • 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.

  7. Results

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

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