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dc.contributorMulti-disciplinary Studiesen_US
dc.contributorJockey Club Rehabilitation Engineering Centreen_US
dc.creatorMak, Chi-chuen-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/3867-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleA study of MRI brain image segmentationen_US
dcterms.abstractThe report summarises certain techniques that have been employed in segmentation of MRI brain images and labeling of the segmented objects, so as to identify the different parts of a human brain, for example, white matter, gray matter, cerebrospinal fluid, etc. These techniques include edge detection, watershed transform, fuzzy C-mean clustering, and Markovian relaxation. Many of these techniques are coming from digital image processing researches, and have been widely applied in different areas. Techniques used in labeling of segmented images are also described, especially Markovain relaxation. Mathematical theory behind some of the techniques is described. Many of which are or have been converted into algorithms and programs. A brief review of past studies of MRI brain segmentation is provided. In the last part of the project, case studies of which certain approaches have been proposed are devoted to application of some of these techniques. The MATLAB platform has been used as the programming tools for realization of these techniques and algorithms. The application of this project could be extended to brain tissue detection and registration, brain trauma location and brain image specification and archiving.en_US
dcterms.extentii, 68 leaves : ill. ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2000en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHBrain -- Imagingen_US
dcterms.LCSHMagnetic resonance imagingen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/3867