We have developed a computer-aided diagnosis scheme for the detection of clustered microcalcifications in mammograms. Using a filter bank, the mammogram image is first decomposed into eight sub-images for extracting nodular patterns and nodular & linear patterns at scales from 1 to 4. Many regions of interest (ROI) with 100×100 matrix sizes are then selected from the mammogram image, and eight features determined in each ROI are obtained from sub-images for nodular patterns and nodular & linear patterns. In the first step of this computerized method for identifying initial candidates, a classifier based on maximum likelihood with eight features is used to distinguish between clustered microcalcifications and normal tissues. In the second step, an artificial neural network with 32 features, including the root-mean-square variation and the first moment of the power spectrum, is employed to reduce false positives (FPs). We evaluated the detection performance of the new scheme using a database of 331 mammograms. The detection scheme had a sensitivity of 96.5% with the number of FPs being 0.69 per mammogram. This computerized method may be a useful tool to assist radiologists in the detection of clustered microcalcifications in mammograms.
