Multimodal brain volume registration using information theory

dc.contributor.advisorBasir, O.A.
dc.contributor.authorSeabrooke, Kevin Christopher
dc.date.accessioned2021-06-04T14:02:21Z
dc.date.available2021-06-04T14:02:21Z
dc.date.copyright2000
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.description.abstractThis thesis is an investigation of techniques used to register medical image volumes of different modalities. A detailed survey of existing image registration technologies is performed. An unstructured, automated technique employing the maximization of mutual information is found to be superior. Using this technology, a registration routine is developed to register three-dimensional brain volumes. The performance of the routine is evaluated using translated and noise-corrupted magnetic resonance and computed tomography brain volumes. The routine is found to achieve sub-voxel accuracy. Two novel approaches are presented to improve the speed of the registration procedure: a seed generating algorithm to reduce the optimizational complexity of the problem, and a selective sampling schedule based on information content to reduce the computational requirements of the cost function. An evaluation of these modifications finds them to reduce overall registration time by two thirds.en_US
dc.identifier.urihttps://hdl.handle.net/10214/25873
dc.language.isoen
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectmedical image volumesen_US
dc.subjectmultimodal brain volume registrationen_US
dc.subjectthree-dimensional brain volumeen_US
dc.subjectseed generating algorithmen_US
dc.subjectselective sampling scheduleen_US
dc.titleMultimodal brain volume registration using information theoryen_US
dc.typeThesisen_US

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