Analysis of observer performance in unknown-location tasks for tomographic image reconstruction
JOSA A, Vol. 24, Issue 12, pp. B99-B109 (2007)
http://dx.doi.org/10.1364/JOSAA.24.000B99
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Abstract
Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.
© 2007 Optical Society of America
OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(110.3000) Imaging systems : Image quality assessment
History
Original Manuscript: March 5, 2007
Revised Manuscript: July 2, 2007
Manuscript Accepted: July 9, 2007
Published: October 8, 2007
Virtual Issues
Vol. 3, Iss. 1 Virtual Journal for Biomedical Optics
Citation
Anastasia Yendiki and Jeffrey A. Fessler, "Analysis of observer performance in unknown-location tasks for tomographic image reconstruction," J. Opt. Soc. Am. A 24, B99-B109 (2007)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-24-12-B99
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