Visual discrimination modeling of lesion detectability

The Sarnoff JNDmetrix visual discrimination model (VDM) was applied to predict human psychophysical performance in the detection of simulated mammographic lesions. Contrast thresholds for the detection of synthetic Gaussian masses on mean backgrounds and simulated mammographic backgrounds were measured in two-alternative, forced-choice (2AFC) trials. Experimental thresholds for 2-D Gaussian signal detection decreased with increasing signal size on mean backgrounds and on 1/f 3 filtered noise images presented with identical (paired) backgrounds. For 2AFC presentations of different (unpaired) filtered noise backgrounds, detection thresholds increased with increasing signal diameter, consistent with a decreasing signal-to-noise ratio. Thresholds for mean and paired filtered noise backgrounds were used to calibrate a new low-pass, spatial-frequency channel in the VDM. The calibrated VDM was able to predict accurate detection thresholds for Gaussian signals on mean and paired 1/f 3 filtered noise backgrounds. To simulate noise-limited detection thresholds for unpaired backgrounds, an approach is outlined for the development of a VDM-based model observer based on statistical decision theory.