Abstract

The Bayesian ideal observer is optimal among all observers and sets an absolute upper bound for the performance of any observer in classification tasks [Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).]. Therefore, the ideal observer should be used for objective image quality assessment whenever possible. However, computation of ideal-observer performance is difficult in practice because this observer requires the full description of unknown, statistical properties of high-dimensional, complex data arising in real life problems. Previously, Markov-chain Monte Carlo (MCMC) methods were developed by Kupinski et al. [J. Opt. Soc. Am. A 20, 430(2003) ] and by Park et al. [J. Opt. Soc. Am. A 24, B136 (2007) and IEEE Trans. Med. Imaging 28, 657 (2009) ] to estimate the performance of the ideal observer and the channelized ideal observer (CIO), respectively, in classification tasks involving non-Gaussian random backgrounds. However, both algorithms had the disadvantage of long computation times. We propose a fast MCMC for real-time estimation of the likelihood ratio for the CIO. Our simulation results show that our method has the potential to speed up ideal-observer performance in tasks involving complex data when efficient channels are used for the CIO.

© 2009 Optical Society of America

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History
Original Manuscript: January 27, 2009
Manuscript Accepted: August 17, 2009
Revised Manuscript: August 1, 2009
Published: October 5, 2009

References

1. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).
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5. S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “A channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136-B150 (2007). [CrossRef]
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7. S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imaging 28, 657-667 (2009). [CrossRef]
8. J. Witten, S. Park, and K. J. Myers, “Using partial least square to compute efficient channels for the Bayesian ideal observer,” Proc. SPIE 7263, 72630Q (2009).
9. S. Park and E. Clarkson, “Markov-chain Monte Carlo for the performance of a channelized-ideal observer in detection tasks with non-Gaussian lumpy backgrounds,” Proc. SPIE 6917, 69170T (2008).
10. C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed. (Springer,2004).
11. H. H. Barrett, C. K. Abbey, and E. Clarkson, “Objective assessment of image quality III: ROC metrics, ideal observers, and likelihood-generating functions,” J. Opt. Soc. Am. A 15, 1520-1535 (1998). [CrossRef]
12. E. Clarkson, “Bounds on the area under the receiver operating characteristic curve for the ideal observer,” J. Opt. Soc. Am. A 19, 1963-1968 (2001). [CrossRef]
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14. E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13(11), 1410-1421 (2006). [CrossRef]
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Author Affiliations

Subok Park

NIBIB/CDRH Laboratory for the Assessment of Medical Imaging Systems

Eric Clarkson

University of Arizona

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