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. Methods ..
Non-linear classifiers preferred over linear classifiers given the randomness in occurrence of tumor cells allowing the trade off that mapping has to be done on a higher dimensional space
Contemporary methods treat as supervised learning problem (Wei et al., 2005)
Support Vector Machines (SVM) (Vapnik et al., 1997)
Kernel Fisher Discriminant (KFD)
Relevance Vector Machines (RVM)

4. Methods ..
SVM was used by Chang et al., on US images to diagnose the nature of tumor
Use of wavelet transform to uncorrelate data (image) (Borges et al., 2001)
Obtain wavelet coefficients as features
Normalize coefficients and feed into Nearest Neighborhood classifier

5. Proposed work
Issues open – need for a classifier that gives more accuracy in lesser time
Design and train an SVM classifier on mammograms
Extend on Borges et al.’s work on wavelet features and use a different classifier on the feature vectors.