Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

Delibasis, KK; Kechriniotis, AI; Tsonos, C; Assimakis, N

dc.contributor.author	Delibasis, KK	en
dc.contributor.author	Kechriniotis, AI	en
dc.contributor.author	Tsonos, C	en
dc.contributor.author	Assimakis, N	en
dc.date.accessioned	2014-03-01T01:32:55Z
dc.date.available	2014-03-01T01:32:55Z
dc.date.issued	2010	en
dc.identifier.issn	0169-2607	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20238
dc.subject	Automatic tracking	en
dc.subject	Vessel diameter measurement	en
dc.subject	Vessel parametric model	en
dc.subject	Vessel segmentation	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Angiographic images	en
dc.subject.other	Automatic algorithms	en
dc.subject.other	Automatic models	en
dc.subject.other	Automatic tracking	en
dc.subject.other	Central axis	en
dc.subject.other	Complex shapes	en
dc.subject.other	Diameter estimation	en
dc.subject.other	Geometric models	en
dc.subject.other	Parametric models	en
dc.subject.other	Positioning error	en
dc.subject.other	Root Mean Square	en
dc.subject.other	Segmentation results	en
dc.subject.other	Sub pixels	en
dc.subject.other	Tracing algorithm	en
dc.subject.other	User intervention	en
dc.subject.other	Vessel detection	en
dc.subject.other	Vessel diameter	en
dc.subject.other	Vessel diameter measurement	en
dc.subject.other	Vessel model	en
dc.subject.other	Vessel parametric model	en
dc.subject.other	Vessel segmentation	en
dc.subject.other	Algorithms	en
dc.subject.other	Image segmentation	en
dc.subject.other	Models	en
dc.subject.other	Servomechanisms	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Volume measurement	en
dc.subject.other	Image matching	en
dc.subject.other	accuracy	en
dc.subject.other	algorithm	en
dc.subject.other	angiography	en
dc.subject.other	article	en
dc.subject.other	automation	en
dc.subject.other	data base	en
dc.subject.other	geometry	en
dc.subject.other	image analysis	en
dc.subject.other	mathematical model	en
dc.subject.other	Algorithms	en
dc.subject.other	Automation	en
dc.subject.other	Humans	en
dc.subject.other	Models, Theoretical	en
dc.subject.other	Retinal Vessels	en
dc.title	Automatic model-based tracing algorithm for vessel segmentation and diameter estimation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cmpb.2010.03.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cmpb.2010.03.004	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation. (C) 2010 Elsevier Ireland Ltd. All rights reserved.	en
heal.publisher	ELSEVIER IRELAND LTD	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
dc.identifier.doi	10.1016/j.cmpb.2010.03.004	en
dc.identifier.isi	ISI:000283040000002	en
dc.identifier.volume	100	en
dc.identifier.issue	2	en
dc.identifier.spage	108	en
dc.identifier.epage	122	en