Abstract: The presence of microcalcifications in mammograms provides an early indication of possible breast cancer. Because of the difficulty associated with visual identification of microcalcifications and the large volume of mammograms read per day, the radiologist stands a good chance of missing some small microcalcification clusters. Although several computer-assisted programs have been developed for the automatic detection of microcalcifications in mammograms, they often generate too many false positives. This paper presents a computer-assisted enhancement technique which is capable of coping with false positive samples. More specifically, a general-purpose clustering algorithm, called Issac (Interactive Selective and Adaptive Clustering), has been developed which achieves a compromise between sensitivity and generalization attributes of existing clustering algorithms. Issac comprises two parts: (i) selective clustering and (ii) interactive adaptation. The first part reduces the number of false positives by identifying sensitive sample domains in the feature space. The second part allows the radiologist to improve results by interactively identifying additional false positive or true negative samples. The clinical evaluation of the results has indicated that the developed enhancement technique has the potential of being an effective mechanism to bring microcalcification areas to the attention of the radiologist during a routine reading session of mammograms. Further clinical evaluation is being carried out for the purpose of full-scale clinical deployment.