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1 April 1994 Observer models for statistically-defined backgrounds
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Abstract
Investigation of human signal-detection performance for noise- limited tasks with statistically defined signal or image parameters represents a step towards clinical realism. However, the ideal observer procedure is then usually nonlinear, and analysis becomes mathematically intractable. Two linear but suboptimal observer models, the Hotelling observer and the non- prewhitening (NPW) matched filter, have been proposed for mathematical convenience. Experiments by Rolland and Barrett involving detection of signals in white noise superimposed on statistically defined backgrounds showed that the Hotelling model gave a good fit while the simple NPW matched filter gave a poor fit. It will be shown that the NPW model can be modified to fit their data by adding a spatial frequency filter of shape similar to the human contrast sensitivity function. The best fit is obtained using an eye filter model, E(f) equals f1.3 exp(-cf2) with c selected to give a peak at 4 cycles per degree.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arthur E. Burgess "Observer models for statistically-defined backgrounds", Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); https://doi.org/10.1117/12.171739
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