A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA

dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Καγκάδης, Γεώργιος Χ.	el
dc.contributor.author	Δασκαλάκη, Αναστασία	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.date.accessioned	2015-05-03T09:14:36Z
dc.date.available	2015-05-03T09:14:36Z
dc.date.issued	2015-05-03
dc.identifier.uri	http://hdl.handle.net/11400/9527
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/S0097849307000532	en
dc.subject	Hybrid
dc.subject	Vessel segmentation
dc.subject	Υβρίδιο
dc.subject	Τμηματοποίηση σκάφους
dc.title	A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA	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.contributorName	Καλατζής, Ιωάννης	el
heal.contributorName	Καραμεσίνη, Μαρία Τ.	el
heal.contributorName	Πέτσας, Θεόδωρος	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.identifier.secondary	doi:10.1016/j.cag.2007.01.020
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Kostopoulos, S., Glotsos, D., Kagadis, G., Daskalakis, A., Spyridonos, P., et al. (June 2007). A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA. Computer and Graphics. 31(3). pp. 493-500. Elsevier Ltd: 2007. Available from: http://www.sciencedirect.com/science/article/pii/S0097849307000532 [Accessed 02/02/2007]	en
heal.abstract	In the present study, a hybrid semi-supervised pixel-based classification algorithm is proposed for the automatic segmentation of intracranial aneurysms in Computed Tomography Angiography images. The algorithm was designed to discriminate image pixels as belonging to one of the two classes: blood vessel and brain parenchyma. Its accuracy in vessel and aneurysm detection was compared with two other reliable methods that have already been applied in vessel segmentation applications: (a) an advanced and novel thresholding technique, namely the frequency histogram of connected elements (FHCE), and (b) the gradient vector flow snake. The comparison was performed by means of the segmentation matching factor (SMF) that expressed how precise and reproducible was the vessel and aneurysm segmentation result of each method against the manual segmentation of an experienced radiologist, who was considered as the gold standard. Results showed a superior SMF for the hybrid (SMF=88.4%) and snake (SMF=87.2%) methods compared to the FHCE (SMF=68.9%). The major advantage of the proposed hybrid method is that it requires no a priori knowledge of the topology of the vessels and no operator intervention, in contrast to the other methods examined. The hybrid method was efficient enough for use in 3D blood vessel reconstruction.	en
heal.publisher	Elsevier Ltd	en
heal.journalName	Computers & Graphics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true