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dc.contributor.advisorDuan, Yeeng
dc.contributor.authorHe, Qingeng
dc.date.issued2011eng
dc.date.issued2011eng
dc.date.submitted2011 Springeng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on November 6, 2012).eng
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.eng
dc.descriptionDissertation advisor: Dr. Ye Duaneng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri-Columbia 2011.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Biochemistry.eng
dc.description"May 2011"eng
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.eng
dc.format.extentxiv, 140 pageseng
dc.identifier.oclc872560217eng
dc.identifier.otherHeQ-101012-D4791eng
dc.identifier.urihttp://hdl.handle.net/10355/15995eng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollection2011 Freely available dissertations (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2011 Dissertationseng
dc.source.originalSubmitted by University of Missouri--Columbia Graduate School.eng
dc.subjectfacial featureeng
dc.subjectshape analysiseng
dc.subjectmedical image analysiseng
dc.subjectbrain morphologyeng
dc.titleShape modeling framework for brain and facial image analysiseng
dc.typeThesiseng
thesis.degree.disciplineBiochemistry (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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