Comparison of rotation invariant local frequency, LBP and SFTA methods for breast abnormality classification

Breast cancer is the second most prominent cancer diagnosed among women. Digital mammography is one of the effective imaging modalities used to detect breast cancer in early stages. Computer-aided detection systems help radiologists to detect and diagnose abnormalities earlier and faster in a mammogram. In this paper, a comprehensive study is carried out on different feature extraction methods for classification of abnormal areas in a mammogram. The prominent techniques used for feature extraction in this study are local binary pattern (LBP), rotation invariant local frequency (RILF) and segmented fractal texture analysis (SFTA). Features extracted from these techniques are then fed to a support vector machine (SVM) classifier for further classification via 10-fold cross-validation method. The evaluation is performed using image retrieval in medical applications (IRMA) database for feature extraction. Our statistical analysis shows that the RILF technique outperforms the LBP and SFTA techniques.