Mass detection in digitized mammograms using two independent computer-assisted diagnosis schemes

OBJECTIVE. Using two independent computer-assisted diagnosis (CAD) schemes, we investigated the potential to improve the sensitivity of mass detection by applying a logical ''or'' operation and to improve the specificity using a logical ''and'' operation. MATERIALS AND METHODS. Two independent mass detectors, one with Gaussian bandpass filtering and multilayer topographic feature analysis and the other with a five-stage search for a single suspicious region, were applied to a large image database that included 428 digitized mammograms with 220 verified masses. The performance of the two schemes and a combination of them in the form of either logical ''or'' or logical ''and'' operations were compared. RESULTS. In this preliminary study, a multilayer topographic feature analysis CAD scheme (CAD-1) achieved a sensitivity of 96% and had a false-positive detection rate of 0.79 per image. A five-stage search method scheme (CAD-2) achieved a sensitivity of 94% and had a false-positive detection rate of 1.69 per image. With an ''or'' operation, the combined results yielded 100% sensitivity with a false-positive detection rate of 2.07 per image. A logical ''and'' operation produced a reduction of the false-positive detection rate to 0.4 per image, but sensitivity also decreased to 90%. CONCLUSION. Similar to an independent double-reading approach and depending upon the relevant clinical question, sensitivity or specificity can be improved by combining the results of several independent CAD schemes.