Detection of glaucomatous change based on vessel shape analysis

Matsopoulos, GK; Asvestas, PA; Delibasis, KK; Mouravliansky, NA; Zeyen, TG

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	https://dspace.lib.ntua.gr/xmlui/handle/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.publisher	PERGAMON-ELSEVIER SCIENCE LTD	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