Abstract
Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Mammography is broadly recognized as an effective imaging modality for the early detection of breast cancer. Computer-aided diagnosis (CAD) systems are very helpful for radiologists in detecting and diagnosing abnormalities earlier and faster than traditional screening programs. An important step of a CAD system is feature extraction. This research gives a comprehensive study of the effects of different features to be used in a CAD system for the classification of masses. The features are extracted using local binary pattern (LBP), which is a texture descriptor, statistical measures, and multi-resolution frameworks. Statistical and LBP features are extracted from each region of interest (ROI), taken from mammogram images, after dividing it into NxN blocks. The multi-resolution features are based on discrete wavelet transform (DWT) and contourlet transform (CT). In multi-resolution analysis, ROIs are decomposed into low sub-band and high sub-bands at different resolution levels and the coefficients of the low sub-band at the last level are taken as features. Support vector machines (SVM) is used for classification. The evaluation is performed using Digital Database for Screening Mammography (DDSM) database. An accuracy of 98.43 is obtained using statistical or LBP features but when both these types of features are fused, the accuracy is increased to 98.63. The CT features achieved classification accuracy of 98.43 whereas the accuracy resulted from DWT features is 96.93. The statistical analysis and ROC curves show that methods based on LBP, statistical measures and CT performs equally well and they not only outperform DWT based method but also other existing methods.