In recent years, extensive research is carried out in Computer Aided Interpretation of digital mammograms for breast cancer classification. Computer aided Interpretation of digital mammograms involves pre-processing, contrast enhancement, segmentation, appropriate feature extraction and classification. Though considerable research is carried out in developing contrast enhancement and image segmentation techniques, cancer regions could not be isolated and extracted efficiently. Also appropriate features which best describe the cancer characteristics were not found. Hence this work focuses on developing efficient image segmentation techniques for isolating the cancer region and also identifying suitable descriptors for describing the cancer region. Modified Expectation Maximization and modified snake algorithm are developed for isolating the abnormality. Area, Minor Axis Length, Major Axis Length, Perimeter, Orientation, Centroid, Eccentricity, EquivDiameter, Solidity and convex area are the features used for describing abnormality. Back Propagation Network is used for determining the presence and absence of cancer in mammograms. Sensitivity of the proposed techniques is 100%.