Automatic Breast Segmentation and Cancer Detection via SVM in Mammograms

Automatic detection of breast cancer in mammograms is a challenging task in Computer Aided Diagnosis (CAD) techniques. This paper presents a simple methodology for breast cancer detection in digital mammograms. Proposed methodology consists of three major steps, i.e. segmentation of breast region, removal of pectoral muscle and classification of breast muscle into normal and abnormal tissues. Segmentation of breast muscle was performed by employing Otsus segmentation technique, afterwards removal of pectoral muscle is carried out by canny edge detection and straight line approximation technique. In next step, Gray Level Co-occurrence Matrices (GLCM) was created form which several features were extracted. At the end, SVM classifier was trained to classify breast region into normal and abnormal tissues. Proposed methodology was validated on Mini-MIAS database and results were compared with previously proposed techniques, which shows that proposed technique can be reliably apply for breast cancer detection.