TY - JOUR
T1 - Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques
AU - Kupinski, Matthew A.
AU - Hoppin, John W.
AU - Clarkson, Eric
AU - Barrett, Harrison H.
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2003/3
Y1 - 2003/3
N2 - 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.
AB - 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.
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U2 - 10.1364/JOSAA.20.000430
DO - 10.1364/JOSAA.20.000430
M3 - Article
C2 - 12630829
AN - SCOPUS:0037361771
VL - 20
SP - 430
EP - 438
JO - Journal of the Optical Society of America A: Optics and Image Science, and Vision
JF - Journal of the Optical Society of America A: Optics and Image Science, and Vision
SN - 1084-7529
IS - 3
ER -