The authors investigated the feasibility of using computer methods for automated detection of clustered microcalcifications on clinical mammograms. A new difference-image approach using a matched filter/box-rim filter combination effectively removed the structured background from the image. A locally adaptive gray-level thresholding technique was then used for extraction of the signals from the resulting difference image. Signal-extraction criteria based on the size, contrast, number, and clustering properties of microcalcifications were next imposed on the detected signals to distinguish true signals from noise or artifacts. The detection accuracy of the computer scheme was evaluated by means of a free response receiver operating characteristic (FROC) analysis. It was found that, for simulated subtle microcalcifications superimposed on normal mammograms, the difference-image approach with a matched filter/box-rim filter combination could yield a true-positive cluster detection rate of 80% at a false-positive detection rate of one cluster per image. In a study of 20 clinical images containing moderately subtle microcalcifications, the automated computer scheme obtained an 82% true-positive cluster detection rate at a false-positive detection rate of one cluster per image. These results indicate that the automated method has the potential to aid radiologists in screening mammograms for clustered microcalcifications.