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The goal of this project is to develop a computer-aided diagnosis (CAD) system for automatic interval change analysis of microcalcification clusters on mammograms. Based on our regional registration method and a search program cluster candidates were detected within the local area on the prior. The cluster on the current image is then paired with the candi-dates to form true (TP-TP) or false (TP-FP) pairs and a correspondence classifier is designed to reduce the (TP-FP). A temporal classifier (TC) based on current and prior information is used if a cluster is detected in the prior, and a current classifier (CurC) based on current information alone is used if no prior cluster is detected. For the TC an LDA, SVM and NN were used. 175 temporal pairs of mammograms were used for evaluation. The registration stage identified 85% (1491175) of the TP-TP pairs with 15 false matches within the 164 image pairs that had detected clusters. The TC based on LDA, SVM and NN achieved a test Az of 0.83, 0.82, 0.84, respectively, for the 164 pairs for classifying the clusters as malignant or benign. For the II clusters without detection on the prior, the test Az by the CurC was 0.72. Four radiologists participated in pilot observer study using our CAD. The average Az in estimating the likelihood of malignancy was 0.70 without CAD and improved to 0.77 with CAD(p=O.04).