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dc.contributor.advisorLiew, Alan
dc.contributor.authorJayasuriya, Surani Anuradhaen_US
dc.date.accessioned2018-01-23T02:31:06Z
dc.date.available2018-01-23T02:31:06Z
dc.date.issued2013en_US
dc.identifier.doi10.25904/1912/1004
dc.identifier.urihttp://hdl.handle.net/10072/366576
dc.description.abstractAdvances in neuroimaging techniques have facilitated the study of anatomical and functional changes in the brain. In order to assist precise diagnosis and treatment, automatic image analysis methods that provide quantitative measures are of great research interests. Accurate brain tissue segmentation of images has been one of the most important research areas for several years. It is an important initial step in neuroimage analysis for applications such as diagnosis of various brain diseases, treatment planning, and studies of various neurological disorders such as Alzheimer’s disease, Schizophrenia, and Multiple sclerosis (MS). However, all these potential applications are crucially dependent on the high accuracy of brain tissue segmentation. Accurate segmentation of MR brain images is difficult since these images contain various noise artifacts. Despite the extensive research, automated analysis of neuroimages still remains a challenging problem. Recently, attention has been turned towards integration of prior knowledge based on anatomical features to improve the accuracy. Based on the fact that the brain exhibits a high level of bilateral symmetry, in this thesis, I explore and discuss the importance of symmetry in the context of tissue classification in MRI, and develop a symmetry-based paradigm for automatic segmentation of brain tissues. Such a classification is motivated by potential radiological applications in assessing brain tissue volume, diagnosis of various brain diseases and treatment planning. The aim of this work is two-fold: First, identifying the location of the symmetry axis or, the symmetry plane becomes imperative. Accurate identification is crucial as it is valuable for the correction of possible misalignment of radiological scans and for symmetry evaluation. In the second stage, automatic classification of brain tissues is done. In other words, the first part of this research focuses on finding the symmetry axis/plane, and the second part develops a segmentation method based on symmetry information.en_US
dc.languageEnglishen_US
dc.publisherGriffith Universityen_US
dc.publisher.placeBrisbaneen_US
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.en_US
dc.subject.keywordsNneuroimaging techniquesen_US
dc.subject.keywordsAlzheimer’s diseaseen_US
dc.subject.keywordsSchizophreniaen_US
dc.subject.keywordsMultiple sclerosisen_US
dc.subject.keywordsMagnetic resonance (MR)en_US
dc.subject.keywordsSymmetry axisen_US
dc.subject.keywordsSymmetry planeen_US
dc.titleApplication of Symmetry Information in Magnetic Resonance Brain Image Segmentationen_US
dc.typeGriffith thesisen_US
gro.facultyScience, Environment, Engineering and Technologyen_US
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorSheridan, Phillip
dc.rights.accessRightsPublicen_US
gro.identifier.gurtIDgu1412221875864en_US
gro.source.ADTshelfnoADT0en_US
gro.source.GURTshelfnoGURTen_US
gro.thesis.degreelevelThesis (PhD Doctorate)en_US
gro.thesis.degreeprogramDoctor of Philosophy (PhD)en_US
gro.departmentSchool of Information and Communication Technologyen_US
gro.griffith.authorJayasuriya, Surani A.


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