Abstract/Details

Signal detection with random backgrounds and random signals


2004 2004

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Abstract (summary)

In this dissertation we explore theoretical and computational methods to investigate Bayesian ideal observers for performing signal-detection tasks. Object models are used to take into account object variability in image backgrounds and signals for the detection tasks. In particular, lumpy backgrounds (LBs) and Gaussian signals are used for various paradigms of signal-detection tasks. Simplified pinhole imaging systems in nuclear medicine are simulated for this work. Markov-chain Monte Carlo (MCMC) methods that estimate the ideal observer test statistic, the likelihood ratio, for signal-known-exactly (SKE) tasks, where signals are nonrandom, are employed. MCMC methods are extended to signal-known-statistically (SKS) tasks, where signals are random. Psychophysical studies for the SKE and SKS tasks using non-Gaussian and Gaussian distributed LBs are conducted. The performance of the Bayesian ideal observer, the human observer, and the channelized-Hotelling observer for the SKE and SKS tasks is compared. Human efficiencies for both the SKE tasks and SKS tasks are estimated. Also human efficiencies for non-Gaussian and Gaussian-distributed LBs are compared for the SKE tasks. Finally, the theory of the channelized-ideal observer (CIO) is introduced to approximate the performance of the ideal observer by the performance of the CIO in cases where the channel outputs of backgrounds and signals are non-Gaussian distributed. Computational approaches to estimate the CIO are investigated.

Indexing (details)


Subject
Radiology;
Mathematics
Classification
0574: Radiology
0405: Mathematics
Identifier / keyword
Health and environmental sciences; Pure sciences; Image quality; Model observers; Object statistics; Signal detection
Title
Signal detection with random backgrounds and random signals
Author
Park, Subok
Number of pages
166
Publication year
2004
Degree date
2004
School code
0009
Source
DAI-B 65/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780496906871, 0496906879
Advisor
Clarkson, Eric
University/institution
The University of Arizona
University location
United States -- Arizona
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3158134
ProQuest document ID
305209229
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305209229
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