Image feature analysis and computer-aided diagnosis in mammography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis.

Abstract

To improve the performance of a computerized scheme for detection of clustered microcalcifications in digitized mammograms, causes of detected false-positive microcalcification signals were analyzed. The false positives were grouped into four categories, namely, microcalcification like noise patterns, artifacts, linear patterns, and others. In an edge-gradient analysis, local edge-gradient values at signal-perimeter pixels of detected microcalcification signals were determined to eliminate false positives that look like subtle microcalcifications or are due to artifacts. In a linear-pattern analysis, the degree of linearity for linear patterns was determined from local gradient values from a set of linear templates oriented in 16 different directions. Threshold values for the edge-gradient analysis and the linear-pattern analysis were determined using a training database of 39 mammograms. It was possible to eliminate 59% and 25%, respectively, of 91 detected false-positive clusters with loss of only 3% of true-positive clusters. The combination of the two methods further improved the scheme in eliminating a total of 73% of the false-positive clusters with loss of 3% of true-positive clusters. Using these thresholds, the two methods were evaluated on another database of 50 mammograms. 62%, 31%, and 80% of the false-positive clusters were eliminated with loss of 3% of true-positive clusters or less, in the edge-gradient analysis, the linear-pattern analysis, and the combination of the two methods, respectively. The edge-gradient analysis and the linear-pattern analysis can reduce the false-positive detection rate, while maintaining a high level of the sensitivity.