The use of a priori information in the detection of mammographic microcalcifications to improve their classification.

In this work, we present a calcification-detection scheme that automatically localizes calcifications in a previously detected cluster in order to generate the input for a cluster-classification scheme developed in the past. The calcification-detection scheme makes use of three pieces of a priori information: the location of the center of the cluster, the size of the cluster, and the approximate number of calcifications in the cluster. This information can be obtained either automatically from a cluster-detection scheme or manually by a radiologist. It is used to analyze only the portion of the mammogram that contains a cluster and to identify the individual calcifications more accurately, after enhancing them by means of a "Difference of Gaussians" filter. Classification performances (patient-based Az=0.92; cluster-based Az=0.72) comparable to those obtained by using manually-identified calcifications (patient-based Az=0.92; cluster-based Az=0.82) can be achieved.