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Automated Segmentation of Retinal Vasculature

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dc.contributor.author Almi'ani, Muder M. en_US
dc.contributor.author Barkana, Buket D. en_US
dc.date.accessioned 2014-07-16T16:46:13Z
dc.date.available 2014-07-16T16:46:13Z
dc.date.issued 2012 en_US
dc.identifier.citation Poster 46 en_US
dc.identifier.other 56a0e68c-b3b1-abcd-f938-1afc2873c73e en_US
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/609
dc.description Image processing, analysis and computer vision techniques are increasing in all fields of medical science, and are especially applicable to modern ophthalmology. Automated image segmentation processing has the prospective for early detection of many diseases like the diabetes, by detecting changes in blood vessel in the retina images . The focus of this poster is on the automated segmentation of vessels in color images of the retina by describes the development of segmentation methodology in the processing of retinal blood vessel images using the region growing method and the Powerlaw transformation . The retina is the only location where blood vessels can be directly visualized non-invasively in vivo. Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease, and stroke. In the same time with suitable feature extraction and automated classification methods, this segmentation method could form the basis of a quick and accurate test for the retina image, which would have many benefits for improved the access to screening people for risk or presence of diseases. en_US
dc.subject Faculty research day en_US
dc.title Automated Segmentation of Retinal Vasculature en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.name Faculty Research Day en_US

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