Automatic detection of clustered microcalcifications in digitized mammogram films

Abstract

The existence of clustered microcalcifications is one of the important early signs of breast cancer. This paper presents an image processing procedure for the automatic detection of clustered microcalcifications in digitized mammograms. In particular, a sensi- tivity range of around one false positive per image is targeted. The proposed method consists of two main steps. First, possible micro- calcification pixels in the mammograms are segmented out using wavelet features or both wavelet features and gray level statistical features, and labeled into potential individual microcalcification ob- jects by their spatial connectivity. Second, individual microcalcifica- tions are detected by using the structure features extracted from the potential microcalcification objects. The classifiers used in these two steps are feedforward neutral networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free response operating character- istics curve is used to evaluate the performance. Results show that the proposed procedure gives quite satisfactory detection perfor- mance. In particular, a 93% mean true positive detection rate is achieved at the price of one false positive per image when both wavelet features and gray level statistical features are used in the first step. © 1999 SPIE and IS&T. (S1017-9909(99)00701-1)