27 March 1996 Observer signal-to-noise ratios for the ML-EM algorithm
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Abstract
We have used an approximate method developed by Barrett, Wilson, and Tsui for finding the ensemble statistics of the maximum likelihood-expectation maximization algorithm to compute task-dependent figures of merit as a function of stopping point. For comparison, human- observer performance was assessed through conventional psychophysics. The results of our studies show the dependence of the optimal stopping point of the algorithm on the detection task. Comparisons of human and various model observers show that a channelized Hotelling observer with overlapping channels is the best predictor of human performance.
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Craig K. Abbey, Craig K. Abbey, Harrison H. Barrett, Harrison H. Barrett, Donald W. Wilson, Donald W. Wilson, } "Observer signal-to-noise ratios for the ML-EM algorithm", Proc. SPIE 2712, Medical Imaging 1996: Image Perception, (27 March 1996); doi: 10.1117/12.236860; https://doi.org/10.1117/12.236860
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