Persistent Link:
http://hdl.handle.net/10150/280729
Title:
Signal detection with random backgrounds and random signals
Author:
Park, Subok
Issue Date:
2004
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
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.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Mathematics.; Health Sciences, Radiology.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Applied Mathematics
Degree Grantor:
University of Arizona
Advisor:
Clarkson, Eric

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleSignal detection with random backgrounds and random signalsen_US
dc.creatorPark, Suboken_US
dc.contributor.authorPark, Suboken_US
dc.date.issued2004en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn 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.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectMathematics.en_US
dc.subjectHealth Sciences, Radiology.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorClarkson, Ericen_US
dc.identifier.proquest3158134en_US
dc.identifier.bibrecord.b48126925en_US
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