Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/28220
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dc.titleSegmentation of Human Muscles of Mastication from Magnetic Resonance Images
dc.contributor.authorNG HSIAO PIAU
dc.date.accessioned2011-11-08T18:02:10Z
dc.date.available2011-11-08T18:02:10Z
dc.date.issued2008-06-02
dc.identifier.citationNG HSIAO PIAU (2008-06-02). Segmentation of Human Muscles of Mastication from Magnetic Resonance Images. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/28220
dc.description.abstractThis thesis presents methods for segmenting the human masticatory muscles from magnetic resonance images that are used in maxillofacial surgeries. Human masticatory muscles directly affect oneb s ability to chew effectively and efficiently. Segmenting them is a challenging task due to close proximity between the muscles and surrounding soft tissue, and the complicated structure of the muscles. An improved watershed segmentation algorithm with unsupervised clustering was introduced to address the drawbacks of the conventional watershed algorithm. Good initializations to the GVF snake were provided automatically in a proposed model-based method while adaptive morphology was introduced in another proposed method to preserve the muscle structure. Dominant slices which together best capture the shape and area features of the muscles were determined and patient-specific muscles models were built using them. These models serve as coarse segmentations which are refined by matching distributions of pixels intensity values within muscles volumes and on muscles boundaries.
dc.language.isoen
dc.subjectSegmentation; Masticatory muscles; Magnetic resonance; three-dimensional imaging;
dc.typeThesis
dc.contributor.departmentNUS GRAD SCH FOR INTEGRATIVE SCI & ENGG
dc.contributor.supervisorFOONG WENG CHIONG, KELVIN
dc.contributor.supervisorONG SIM HENG
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
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