Computer aided detection and Diagnosis systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, a computer aided system for breast cancer diagnosis in mammographic images is presented. In particular, the method looks for microcalcification cluster occurrence and makes the diagnosis of the detected abnormality. The procedure first detects microcalcifications having a cluster pattern and then classifies the abnormalities as benign or malignant clusters. The method formulates the differentiation between malignant and benign microcalcification clusters as a supervised learning problem implementing an artificial neural network classifier. As input to the classifier, the procedure uses image features automatically extracted from the detected clusters. The seven features used are related both to the distribution of microcalcifications within cluster and to the uniformity of their shape. The performance of the implemented system is evaluated taking into account the accuracy of classifying clusters. The obtained results make this method able to operate as a "second opinion" helping radiologists during the routine clinical practice. Moreover, the implemented method has a general validity and can be used to detect and to classify microcalcification clusters independently from the acquisition equipment adopted during the mammographic screening.
Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI
