Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms.

Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.