Observer signal-to-noise ratios for the ML-EM algorithm

Craig K. Abbey; Harrison H. Barrett; Donald W. Wilson

doi:10.1117/12.236860

27 March 1996 Observer signal-to-noise ratios for the ML-EM algorithm

Craig K. Abbey, Harrison H. Barrett, Donald W. Wilson

Proceedings Volume 2712, Medical Imaging 1996: Image Perception; (1996) https://doi.org/10.1117/12.236860
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

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.

Citation Download Citation

Craig K. Abbey, Harrison H. Barrett, and Donald W. Wilson "Observer signal-to-noise ratios for the ML-EM algorithm", Proc. SPIE 2712, Medical Imaging 1996: Image Perception, (27 March 1996); https://doi.org/10.1117/12.236860

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