A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films

Clusters of microcalcifications in mammograms are an important early sign o f breast cancer. This paper presents a computer aided diagnosis (CAD) syste m for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potenti al microcalcification pixels in the mammograms are segmented out by using m ixed features consisting of wavelet features and gray level statistical fea tures, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are dete cted by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is a nalyzed using general regression neural networks via sequential forward and sequential backward selection methods, The classifiers used in these two s teps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) c urve is used to evaluate the performance, Results show that the proposed sy stem gives quite satisfactory detection performance. In particular, a 90% m ean true positive detection rate is achieved at the cost of 0.5 false posit ive per image when mixed features are used in the first step and 15 feature s selected by the sequential backward selection method are used in the seco nd step, However, we must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.