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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 11 — Nov. 1, 2009
  • pp: B59–B71

Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds

Subok Park and Eric Clarkson  »View Author Affiliations


JOSA A, Vol. 26, Issue 11, pp. B59-B71 (2009)
http://dx.doi.org/10.1364/JOSAA.26.000B59


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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

OCIS Codes
(110.3000) Imaging systems : Image quality assessment
(330.1880) Vision, color, and visual optics : Detection
(330.6100) Vision, color, and visual optics : Spatial discrimination

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

Virtual Issues
Vol. 4, Iss. 13 Virtual Journal for Biomedical Optics
October 8, 2009 Spotlight on Optics

Citation
Subok Park and Eric Clarkson, "Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds," J. Opt. Soc. Am. A 26, B59-B71 (2009)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-11-B59


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References

  1. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).
  2. M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal observer computation using Markov-chain Monte Carlo,” J. Opt. Soc. Am. A 20, 430-438 (2003). [CrossRef]
  3. X. He, B. S. Caffo, and E. Frey, “Toward realistic and practical ideal observer estimation for the optimization of medical imaging systems,” IEEE Trans. Med. Imaging 27, 1535-1543 (2008). [CrossRef] [PubMed]
  4. C. K. Abbey and J. M. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in Digital Mammography, E.A.Krupinski, ed., Vol. 5116 of Lecture Notes in Computer Science, (Springer-Verlag, 2008), pp. 393-400. [CrossRef]
  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]
  6. B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725-1738 (2003). [CrossRef]
  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] [PubMed]
  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). [CrossRef]
  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). [CrossRef]
  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]
  13. J. P. Rolland and H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649-658 (1992). [CrossRef] [PubMed]
  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] [PubMed]
  15. B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353-362 (2006). [CrossRef] [PubMed]

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