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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 20, Iss. 3 — Mar. 1, 2003
  • pp: 430–438

Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques

Matthew A. Kupinski, John W. Hoppin, Eric Clarkson, and Harrison H. Barrett  »View Author Affiliations

JOSA A, Vol. 20, Issue 3, pp. 430-438 (2003)

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The ideal observer sets an upper limit on the performance of an observer on a detection or classification task. The performance of the ideal observer can be used to optimize hardware components of imaging systems and also to determine another observer’s relative performance in comparison with the best possible observer. The ideal observer employs complete knowledge of the statistics of the imaging system, including the noise and object variability. Thus computing the ideal observer for images (large-dimensional vectors) is burdensome without severely restricting the randomness in the imaging system, e.g., assuming a flat object. We present a method for computing the ideal-observer test statistic and performance by using Markov-chain Monte Carlo techniques when we have a well-characterized imaging system, knowledge of the noise statistics, and a stochastic object model. We demonstrate the method by comparing three different parallel-hole collimator imaging systems in simulation.

© 2003 Optical Society of America

OCIS Codes
(030.4280) Coherence and statistical optics : Noise in imaging systems
(110.2960) Imaging systems : Image analysis
(110.3000) Imaging systems : Image quality assessment

Original Manuscript: June 10, 2002
Revised Manuscript: November 21, 2002
Manuscript Accepted: November 25, 2002
Published: March 1, 2003

Matthew A. Kupinski, John W. Hoppin, Eric Clarkson, and Harrison H. Barrett, "Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques," J. Opt. Soc. Am. A 20, 430-438 (2003)

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