Detection of glaucomatous change based on vessel shape analysis

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dc.contributor.author Matsopoulos, GK en
dc.contributor.author Asvestas, PA en
dc.contributor.author Delibasis, KK en
dc.contributor.author Mouravliansky, NA en
dc.contributor.author Zeyen, TG en
dc.date.accessioned 2014-03-01T01:28:08Z
dc.date.available 2014-03-01T01:28:08Z
dc.date.issued 2008 en
dc.identifier.issn 0895-6111 en
dc.identifier.uri http://hdl.handle.net/123456789/18722
dc.subject Artificial neural networks en
dc.subject Classification en
dc.subject Glaucoma en
dc.subject k-Nearest Neighbor classifier en
dc.subject Registration en
dc.subject Self organizing maps en
dc.subject Sequential float forward search en
dc.subject Wavelet coefficients en
dc.subject.classification Radiology, Nuclear Medicine & Medical Imaging en
dc.subject.other Image analysis en
dc.subject.other Image classification en
dc.subject.other Image segmentation en
dc.subject.other Parameter estimation en
dc.subject.other Patient treatment en
dc.subject.other Risk analysis en
dc.subject.other Self organizing maps en
dc.subject.other Classification techniques en
dc.subject.other Glaucomatous damage en
dc.subject.other Optic nerve head en
dc.subject.other Sequential float forward search en
dc.subject.other Wavelet coefficients en
dc.subject.other Medical imaging en
dc.subject.other angiogenesis en
dc.subject.other article en
dc.subject.other artificial neural network en
dc.subject.other camera en
dc.subject.other correspondence analysis en
dc.subject.other diagnostic imaging en
dc.subject.other disease classification en
dc.subject.other glaucoma en
dc.subject.other human en
dc.subject.other image processing en
dc.subject.other intraocular hypertension en
dc.subject.other major clinical study en
dc.subject.other optic disk en
dc.subject.other priority journal en
dc.subject.other quantitative analysis en
dc.subject.other retina blood vessel en
dc.subject.other retina image en
dc.subject.other Glaucoma, Open-Angle en
dc.subject.other Humans en
dc.subject.other Image Interpretation, Computer-Assisted en
dc.subject.other Intraocular Pressure en
dc.subject.other Neural Networks (Computer) en
dc.subject.other Ocular Hypertension en
dc.subject.other Optic Disk en
dc.subject.other Optic Nerve Diseases en
dc.subject.other Photography en
dc.subject.other Retinal Vessels en
dc.subject.other Sensitivity and Specificity en
dc.subject.other Stereotaxic Techniques en
dc.title Detection of glaucomatous change based on vessel shape analysis en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.compmedimag.2007.11.003 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.compmedimag.2007.11.003 en
heal.language English en
heal.publicationDate 2008 en
heal.abstract Glaucoma, a leading cause of blindness worldwide, is a progressive optic neuropathy with characteristic structural changes in the optic nerve head and concomitant visual field defects. Ocular hypertension (i.e. elevated intraocular pressure without glaucoma) is the most important risk factor to develop glaucoma. Even though a number of variables, including various optic disc and visual field parameters, have been used in order to identify early glaucomatous damage, there is a need for computer-based methods that can detect early glaucomatous progression so that treatment to prevent further progression can be initiated. This paper is focused on the description of a system based on image processing and classification techniques for the estimation of quantitative parameters to define vessel deformation and the classification of image data into two classes: patients with ocular hypertension who develop glaucomatous damage and patients with Ocular hypertension who remain stable. The proposed system consists of the retinal image preprocessing module for vessel central axis segmentation, the automatic retinal image registration module based on a novel application of self organizing maps (SOMs) to define automatic point correspondence, the retinal vessel attributes calculation module to select the vessel shape attributes and the data classification module, using an artificial neural network classifier, to perform the necessary subject classification. Implementation of the system to optic disc data from 127 subjects obtained by a fundus camera at regular intervals provided a classification rate of 87.5%, underscoring the value of the proposed system to assist in the detection of early glaucomatous change. (C) 2007 Elsevier Ltd. All rights reserved. en
heal.journalName Computerized Medical Imaging and Graphics en
dc.identifier.doi 10.1016/j.compmedimag.2007.11.003 en
dc.identifier.isi ISI:000254846100003 en
dc.identifier.volume 32 en
dc.identifier.issue 3 en
dc.identifier.spage 183 en
dc.identifier.epage 192 en

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