Novel Margin Features for Mammographic Mass Classification

Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. Masses are one type of these abnormalities which are mostly characterized by their margin and shape. For classification of masses proper features are needed to be extracted. However, the number of well-known features for describing margin is much fewer than geometrical, shape, and textural ones. In addition, most of the existing margin features are highly dependent on segmentation accuracy. In this work, new features for describing margin of masses are presented which can handle inaccuracies in segmentation. These features are obtained from a set of waveforms by wavelet analysis among each of them. For each of these waveforms an edge probability distribution is computed. Then, features are extracted from these probability distributions. Although these features are called margin features, they are highly related to the texture of the mass. For experimentation DDSM dataset was used and our simulations show the great performance of these features in classification of masses.