Human performance for detection and discrimination of simulated microcalcifications in mammographic backgrounds

We conducted experiments to determine human performance in detecting and discriminating microcalcification-like objects in mammographic background. This study is an extension of our previous work where we investigated detection and discrimination of known objects in white noise background (SKE/BKE taks). In the present experiments, we used hybrid images, consisting of computer-generated images of three signal shapes which were added into mammographic background extracted from digitized normal mammograms. Human performance was measured by determining percentage correct (PC) in 2-AFC experiments for the tasks of detecting a signal or discriminating between two signal shapes. PC was converted into a detection or discrimination index d' and psychometric functions were created by plotting d' as function of square root of signal energy. Human performance was compared to predictions of a NPWE model observer. We found that the slope of the linear portion of the psychometric function for detection was smaller than that for discrimination, as opposed to what we we observed for white noise backgrounds, where the psychometric function for detection was significantly steeper than that for discrimination. We found that human performance was qualitatively reproduced by model observer predictions.