  A recently proposed model observer mimics the foveated nature of the human
visual system by processing the entire image with varying spatial detail,
executing eye movements and scrolling through slices. The model can predict how
human search performance changes with signal type and modality (2D vs. 3D), yet
its implementation is computationally expensive and time-consuming. Here, we
evaluate various image quality metrics using extensions of the classic index of
detectability expressions and assess foveated model observers for
location-known exactly tasks. We evaluated foveated extensions of a Channelized
Hotelling and Non-prewhitening model with an eye filter. The proposed methods
involve calculating a model index of detectability (d') for each retinal
eccentricity and combining these with a weighting function into a single
detectability metric. We assessed different versions of the weighting function
that varied in the required measurements of the human observers' search (no
measurements, eye movement patterns, and size of the image and median search
times). We show that the index of detectability across eccentricities weighted
using the eye movement patterns of observers best predicted human performance
in 2D vs. 3D search performance for a small microcalcification-like signal and
a larger mass-like. The metric with weighting function based on median search
times was the second best at predicting human results. The findings provide a
set of model observer tools to evaluate image quality in the early stages of
imaging system evaluation or design without implementing the more
computationally complex foveated search model.
