Detection of glaucomatous change based on vessel shape analysis

dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Δελήμπασης, Κωνσταντίνος Κ.	el
dc.contributor.author	Μουραβλιάνσκυ, Νικόλαος Α.	el
dc.contributor.author	Zeyen, Thierry G.	en
dc.date.accessioned	2015-02-09T08:19:22Z
dc.date.available	2015-02-09T08:19:22Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5898
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com	en
dc.subject	Glaucoma
dc.subject	Registration
dc.subject	Γλαύκωμα
dc.subject	Εγγραφή
dc.title	Detection of glaucomatous change based on vessel shape analysis	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85055227
heal.identifier.secondary	doi:10.1016/j.compmedimag.2007.11.003
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2008
heal.bibliographicCitation	Matsopoulos, G., Asvestas, P., Delibasis, K., Mouravliansky, N. and Zeyen, T. (April 2008). Detection of glaucomatous change based on vessel shape analysis. Computerized Medical Imaging and Graphics. 32(3). pp. 183-192. Elsevier Science Ltd. Available from: http://www.sciencedirect.com [Accessed 10/01/2008]	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.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Computerized Medical Imaging and Graphics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true