  Within the framework of a virtual clinical trial for breast imaging, we aim
to develop numerical observers that follow the same detection performance
trends as those of a typical human observer. In our prior work, we showed that
by including spatiotemporal contrast sensitivity function (stCSF) of human
visual system (HVS) in a multi-slice channelized Hotelling observer (msCHO), we
can correctly predict trends of a typical human observer performance with the
viewing parameters of browsing speed, viewing distance and contrast. In this
work we further improve our numerical observer by modeling contrast masking.
After stCSF, contrast masking is the second most prominent property of HVS and
it refers to the fact that the presence of one signal affects the visibility
threshold for another signal. Our results indicate that the improved numerical
observer better predicts changes in detection performance with background
complexity.
