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

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 26, Issue 11, 2009, Pages B59-B71

  Park, Subok ^a   Clarkson, Eric ^b  

^a CENTER FOR DEVICES AND RADIOLOGICAL HEALTH   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER SIMULATION; HUMAN; METHODOLOGY; MONTE CARLO METHOD; NORMAL DISTRIBUTION; OPTICS; PROBABILITY; REPRODUCIBILITY; STATISTICAL MODEL; VISION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; HUMANS; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; OPTICS AND PHOTONICS; PATTERN RECOGNITION, AUTOMATED; PROBABILITY; REPRODUCIBILITY OF RESULTS; VISION, OCULAR;

ESTIMATION; IMAGE QUALITY; INFORMATION THEORY; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; OPTICAL SYSTEMS; PROGRAMMABLE LOGIC CONTROLLERS;

BAYESIAN; CHANNELIZED-IDEAL OBSERVERS; CLASSIFICATION TASKS; COMPLEX BACKGROUND; COMPLEX DATA; COMPUTATION TIME; EFFICIENT CHANNELS; EFFICIENT ESTIMATION; HIGH-DIMENSIONAL; IDEAL OBSERVERS; LIKELIHOOD RATIOS; MARKOV CHAIN MONTE CARLO; MODULATION THEORY; NON-GAUSSIAN; OBJECTIVE IMAGE QUALITY ASSESSMENT; OBSERVER PERFORMANCE; REAL-LIFE PROBLEMS; REAL-TIME ESTIMATION; SIMULATION RESULT; SPEED-UPS; STATISTICAL PROPERTIES; UPPER BOUND;

COMPUTATIONAL EFFICIENCY;

EID: 70449709542     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.26.000B59     Document Type: Article

References (15)

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).
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).
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.
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).
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).
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).
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).
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).
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).
14. E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13(11), 1410-1421 (2006).
15. B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353-362 (2006).