  We investigate improvements to our 3D model observer with the goal of better
matching human observer performance as a function of viewing distance,
effective contrast, maximum luminance, and browsing speed. Two nonlinear
methods of applying the human contrast sensitivity function (CSF) to a 3D model
observer are proposed, namely the Probability Map (PM) and Monte Carlo (MC)
methods. In the PM method, the visibility probability for each frequency
component of the image stack, p, is calculated taking into account Barten's
spatiotemporal CSF, the component modulation, and the human psychometric
function. The probability p is considered to be equal to the perceived
amplitude of the frequency component and thus can be used by a traditional
model observer (e.g., LG-msCHO) in the space-time domain. In the MC method,
each component is randomly kept with probability p or discarded with 1-p. The
amplitude of the retained components is normalized to unity. The methods were
tested using DBT stacks of an anthropomorphic breast phantom processed in a
comprehensive simulation pipeline. Our experiments indicate that both the PM
and MC methods yield results that match human observer performance better than
the linear filtering method as a function of viewing distance, effective
contrast, maximum luminance, and browsing speed.
