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dc.contributor.authorGholami, Peyman 15:17:05 (GMT) 15:17:05 (GMT)
dc.description.abstractVision loss, with a prevalence loss greater than 42 million in the United States is one of the major challenges of today's health-care industry and medical science. Early detection of different retinal-related diseases will dramatically reduce the risk of vision loss. Optical Coherence Tomography (OCT) is a relatively new imaging technique which is of great importance in the identification of ocular and especially retinal diseases. Thus, the efficient analysis of OCT images provides several advantages. In this thesis, we propose a series of image processing and machine learning techniques for the automated analysis of OCT images. The proposed methodology in chapter 2 localizes different retinal layers using a modified version of active contour models. In chapter 3, we propose a method which classifies OCT images based on different pathological conditions using novel methods, e.g., transfer learning and new texture detection techniques. The proposed methods along with the clinically meaningful extracted characteristics provide numbers of applications and benefits, e.g., saving a considerable amount of time and providing more-efficient and -accurate indices for the diagnosis and treatment of different ocular diseases to ophthalmologists and finally reducing the overall risk of vision loss.en
dc.publisherUniversity of Waterlooen
dc.subjectOptical Coherence Tomographyen
dc.subjectImage analysisen
dc.subjectMachine learningen
dc.subjectImage classificationen
dc.subjectImage segmentationen
dc.subjectOcular diseasesen
dc.subjectTransfer learningen
dc.titleDeveloping algorithms for the analysis of retinal Optical Coherence Tomography imagesen
dc.typeMaster Thesisen
dc.pendingfalse of Optometry and Vision Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Scienceen
uws.contributor.advisorLakshminarayanan, Vasudevan
uws.contributor.advisorZelek, John
uws.contributor.affiliation1Faculty of Scienceen

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