Automatic characterization of masses in mammograms

The classification of benign and malignant masses in digital mammogram is an important yet challenging step for the early detection of breast cancer. This paper presents statistical measures of the orientation of texture to classify malignant and benign masses. Since the presence of mass in mammogram may change the orientation of normal breast tissues, two types of co-occurrence matrices are derived to estimate the joint occurrence of the angles of oriented structures for characterizing them. Haralick's 14 features are then extracted from each of the matrices derived from different regions related to mass. A total of 444 mass regions from 434 scanned-film images of the DDSM database are selected to evaluate the performance of the proposed features to differentiate the masses. The features are also compared with Haralick's features, obtained from well-known gray-level co-occurrence matrix. The best Az value of 0.77 is achieved with the stepwise logistic regression method for feature selection, an Fisher linear discriminant analysis for classification, and the leave-one-ROI-out approach for cross validation.