Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 430-438 |
| Number of pages | 9 |
| Journal | Journal of the Optical Society of America A: Optics and Image Science, and Vision |
| Volume | 20 |
| Issue number | 3 |
| State | Published - 2003 |
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ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Computer Vision and Pattern Recognition
Cite this
Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques. / Kupinski, Matthew A; Hoppin, John W.; Clarkson, Eric W; Barrett, Harrison H.
In: Journal of the Optical Society of America A: Optics and Image Science, and Vision, Vol. 20, No. 3, 2003, p. 430-438.Research output: Contribution to journal › Article
}
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 W
AU - Barrett, Harrison H
PY - 2003
Y1 - 2003
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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UR - http://www.scopus.com/inward/citedby.url?scp=0037361771&partnerID=8YFLogxK
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 -