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dc.contributor.advisorDuan, Yeen_US
dc.contributor.authorHe, Qing
dc.date.issued2011
dc.date.submitted2011 Springen_US
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on November 6, 2012).en_US
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.en_US
dc.descriptionDissertation advisor: Dr. Ye Duanen_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionVita.en_US
dc.descriptionPh. D. University of Missouri-Columbia 2011.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Biochemistry.en_US
dc.description"May 2011"en_US
dc.description.abstractThe field of medical image analysis has greatly influenced the practice of neuroscience. Many studies aim to find new disease related anatomical characteristics based on the analysis of Magnetic Resonance images (MRI). Image segmentation is usually a fundamental step, in which brain structures of interest are delineated from MRI. Neuroscientists then categorize segmentations of anatomical structures by attributes such as shape, size, or location. Besides medical images, biometric images such as facial images are gaining more and more attention from clinical research because certain biometric features are also related to neurological diseases. Similar to brain morphology analysis, facial features are collected for studies testing the hypothesis that the disease alters the facial shape. Despite the rapid development of image analysis technologies, most clinical practice still relies on laborious manual. The aim of this thesis is to develop a shape modeling framework for brain and facial image analysis, for the purpose of clinical practice. Specifically, it focuses on the following three problems: (1) brain structure segmentation from MRI, where several deformable model based segmentation methods are proposed; (2) quantitative analysis of brain morphology, where methods for directly comparing 3D shapes of brain structures are proposed, and clinical applications are demonstrated; (3) facial morphology analysis, where an automatic facial feature localization method is proposed, and applied to the shape analysis of the eyes.en_US
dc.format.extentxiv, 140 pagesen_US
dc.identifier.otherHeQ-101012-D4791
dc.identifier.urihttp://hdl.handle.net/10355/15995
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartofcollection2011 Freely available dissertations (MU)
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2011 Dissertations
dc.subjectfacial featureen_US
dc.subjectshape analysisen_US
dc.subjectmedical image analysisen_US
dc.subjectbrain morphologyen_US
dc.titleShape modeling framework for brain and facial image analysisen_US
dc.typeThesisen_US
thesis.degree.disciplineBiochemistryeng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoralen_US
thesis.degree.namePh. D.en_US


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