Breast Tissue Density Classification in Mammograms Based on Supervised Machine Learning Technique

Breast tissue density is one of the symptoms for breast cancer detection. Fully automatic breast tissue density classification is presented in this work. Present work consists of four steps which include breast region extraction and enhancement of mammograms, segmentation, feature extraction, and breast tissue density classification. Enhancement of mammogram is done by applying fractional order differential based filter. Segmentation of breast tissue segmentation has been done by using clustering based fast fuzzy c-means technique. Further, texture based local binary pattern (LBP) and dominant rotated local binary pattern (DRLBP) features have been computed from the extracted breast tissues to characterize its texture property. Support vector machine with linear kernel functions are used to classify the breast tissue density. Proposed algorithm is validated on the publicly available 322 mammograms of Mini-Mammographic Image Analysis Society (MIAS).