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

dc.contributor.author	Δελήμπασης, Κωνσταντίνος Κ.	el
dc.contributor.author	Κεχρινιώτης, Αριστείδης Ι.	el
dc.contributor.author	Τσώνος, Χρήστος	el
dc.contributor.author	Ασημάκης, Νικόλαος Δ.	el
dc.date.accessioned	2015-05-17T09:49:12Z
dc.date.available	2015-05-17T09:49:12Z
dc.date.issued	2015-05-17
dc.identifier.uri	http://hdl.handle.net/11400/10579
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.sciencedirect.com/science/article/pii/S0169260710000453	en
dc.subject	Vessel segmentation
dc.subject	Automatic tracking
dc.subject	Τμηματοποίηση σκάφους
dc.subject	Αυτόματη παρακολούθηση
dc.title	Automatic model-based tracing algorithm for vessel segmentation and diameter estimation	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/sh85010114
heal.identifier.secondary	doi:10.1016/j.cmpb.2010.03.004
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010
heal.bibliographicCitation	Delibasis, K., Kechriniotis, A., Tsonos, C. and Assimakis, N. (November 2010). Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Computer Methods and Programs in Biomedicine. 100(2). pp. 108-122. Elsevier Ireland Ltd: 2010. Available from: http://www.sciencedirect.com/science/article/pii/S0169260710000453 [Accessed03/04/2010]	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.	en
heal.publisher	Elsevier Ireland Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true