Breast Density Classification in Mammogram Images

Breast cancer is the common type of cancer in the world, which is most common among women. It is found that there is a correlation between the breast cancer and breast density, hence there is a need for developing a method that identifies the breast density so as to reduce morbidity and mortality. The aim of this work is to develop a Computer Aided Design (CAD) system that classifies the breast density according to the Breast Imaging-Reporting and Data System (BI-RADS) standard with the help of the digital mammographic images. To this end, we propose an effective feature descriptor, namely Locally Encoded Transform feature histogram (LETRIST) for capturing the essential characteristics that discriminates across the density categories. Proposed descriptor evaluated using Support Vector Machine (SVM) on the Mammographic Image Analysis Society (MIAS) database demonstrates the efficiency of proposed system.