Effects of background complexity on LROC analysis of SPECT

We investigate image quality assessment for SPECT for the case where the human observer must detect and locate a lesion in the noisy reconstructed image. The lesion can appear anywhere in a search region which may contain a complex background of hot and cold structures. Our hypothesis is that as the spatial complexity of the background increases, the performance of the human observer decreases. In this study, the background is not random, but is fixed. We consider four backgrounds with increasing complexity. Human performance is measured using a two-alternative forced-choice (2AFC) test. From the 2AFC results, one can compute a measure of human performance, the area under LROC curve. We observe that the human performance degrades as the background complexity increases despite the fact that the true background image is available to the observer during the 2AFC test. Therefore, the human apparently has a difficult time learning complex backgrounds. We also compute the performance of an ideal observer for this task, and show that it is insensitive to background complexity.