Ipsilateral multi-view CAD system for mass detection in digital mammography

Abstract

An ipsilateral multi-view computer-aided diagnosis (CAD) scheme is presented for the earlier mass detection in digital mammograms. Tree structured nonlinear filtering (TSF) is used in image noise suppression. Two wavelet-based methods, directional wavelet transform (DWT) and tree structured wavelet transform (TSWT) are employed for image enhancement. Adaptive fuzzy-C means (FCM) algorithm is conducted for segmentation. Concurrent analysis is employed for iterative analysis of ipsilateral multi-view mammograms to raise detection sensitivity and specificity, and a supervised three-layer artificial neural network (ANN) in which the backpropagation (BP) algorithm combined with Kalman filtering is used as training algorithm is developed as a classifier, which has been trained using the training database with biopsy proven truth files. The application of such CAD system in digital mammography is reported in this article. The test database consists of 200 cases in which the distribution of normal, abnormal cases balanced, and free-response receiver operating characteristic (FROC) analysis method is used to test the performance of the developed unilateral CAD system. The performance comparison has been conducted between the final ipsilateral mufti-view CAD system and current single-view CAD system. The study results have shown that the advantages of ipsilateral mufti-view CAD method over current single-view CAD system express the feasibility of ipsilateral multi-view CAD system combined with concurrent analysis method described in this paper for the improvement of overall performance of CAD system in the early stage mass detection.