A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis Society's MiniMammogram database are used. The true positive and false positive rates are used to evaluate the performance of the classification, and the results are compared with those from other studies presented in the literature. The results show that the classification method of unsharp masking and low-band eliminated image statistics is convenient for MC cluster detection. In particular, a true positive rate of about 94% is achieved at the rate of 0.06 false positives per image.