Portal de Periódicos da CAPES

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

1998; Wiley; Volume: 25; Issue: 8 Linguagem: Inglês

10.1118/1.598326

2473-4209

Rufus H. Nagel, Robert M. Nishikawa, John Papaioannou, Kunio Doi,

Colorectal Cancer Screening and Detection

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.