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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsHarold L. Kundel
Pages47-58
Number of pages12
StatePublished - Jan 1 1996
EventMedical Imaging 1996: Image Perception - Newport Beach, CA, USA
Duration: Feb 14 1996Feb 14 1996

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2712
ISSN (Print)0277-786X

Other

OtherMedical Imaging 1996: Image Perception
CityNewport Beach, CA, USA
Period2/14/962/14/96

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Abbey, C. K., Barrett, H. H., & Wilson, D. W. (1996). Observer signal-to-noise ratios for the ML-EM algorithm. In H. L. Kundel (Ed.), Proceedings of SPIE - The International Society for Optical Engineering (pp. 47-58). (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 2712).