Persistent Link:
http://hdl.handle.net/10150/612941
Title:
Staging Liver Fibrosis with Statistical Observers
Author:
Brand, Jonathan Frieman
Issue Date:
2016
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:
Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1mm, which close to the resolution limit of in vivo Gd-enhanced MRI. In this work the methods to collect training and testing images for a Hotelling observer are covered. An observer based on local texture analysis is trained and tested using wet-tissue phantoms. The technique is used to optimize the MRI sequence based on task performance. The final method developed is a two stage model observer to classify fibrotic and healthy tissue in both phantoms and in vivo MRI images. The first stage observer tests for the presence of local texture. Test statistics from the first observer are used to train the second stage observer to globally sample the local observer results. A decision of the disease class is made for an entire MRI image slice using test statistics collected from the second observer. The techniques are tested on wet-tissue phantoms and in vivo clinical patient data.
Type:
text; Electronic Dissertation
Keywords:
hepatic fibrosis; Hotelling observer; magnetic resonance imaging; optimazation; texture analysis; Optical Sciences; classification
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Optical Sciences
Degree Grantor:
University of Arizona
Advisor:
Furenlid, Lars R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleStaging Liver Fibrosis with Statistical Observersen_US
dc.creatorBrand, Jonathan Friemanen
dc.contributor.authorBrand, Jonathan Friemanen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
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
dc.description.abstractChronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1mm, which close to the resolution limit of in vivo Gd-enhanced MRI. In this work the methods to collect training and testing images for a Hotelling observer are covered. An observer based on local texture analysis is trained and tested using wet-tissue phantoms. The technique is used to optimize the MRI sequence based on task performance. The final method developed is a two stage model observer to classify fibrotic and healthy tissue in both phantoms and in vivo MRI images. The first stage observer tests for the presence of local texture. Test statistics from the first observer are used to train the second stage observer to globally sample the local observer results. A decision of the disease class is made for an entire MRI image slice using test statistics collected from the second observer. The techniques are tested on wet-tissue phantoms and in vivo clinical patient data.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjecthepatic fibrosisen
dc.subjectHotelling observeren
dc.subjectmagnetic resonance imagingen
dc.subjectoptimazationen
dc.subjecttexture analysisen
dc.subjectOptical Sciencesen
dc.subjectclassificationen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineOptical Sciencesen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorFurenlid, Lars R.en
dc.contributor.committeememberAltbach, Maria I.en
dc.contributor.committeememberKupinski, Matthew A.en
dc.contributor.committeememberFurenlid, Lars R.en
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