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

Kostopoulos, S; Glotsos, D; Kagadis, GC; Daskalakis, A; Spyridonos, P; Kalatzis, I; Karamessini, M; Petsas, T; Cavouras, D; Nikiforidis, G

dc.contributor.author	Kostopoulos, S	en
dc.contributor.author	Glotsos, D	en
dc.contributor.author	Kagadis, GC	en
dc.contributor.author	Daskalakis, A	en
dc.contributor.author	Spyridonos, P	en
dc.contributor.author	Kalatzis, I	en
dc.contributor.author	Karamessini, M	en
dc.contributor.author	Petsas, T	en
dc.contributor.author	Cavouras, D	en
dc.contributor.author	Nikiforidis, G	en
dc.date.accessioned	2014-03-01T01:56:16Z
dc.date.available	2014-03-01T01:56:16Z
dc.date.issued	2007	en
dc.identifier.issn	00978493	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28032
dc.subject	CTA	en
dc.subject	FHCE	en
dc.subject	Hybrid	en
dc.subject	Snake	en
dc.subject	Vessel segmentation	en
dc.subject.other	Algorithms	en
dc.subject.other	Blood vessels	en
dc.subject.other	Image reconstruction	en
dc.subject.other	Image segmentation	en
dc.subject.other	Medical imaging	en
dc.subject.other	Radiology	en
dc.subject.other	Computed Tomography Angiography	en
dc.subject.other	Pixel-based classification	en
dc.subject.other	Vessel segmentation	en
dc.subject.other	Classification (of information)	en
dc.title	A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cag.2007.01.020	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cag.2007.01.020	en
heal.publicationDate	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. © 2007 Elsevier Ltd. All rights reserved.	en
heal.journalName	Computers and Graphics (Pergamon)	en
dc.identifier.doi	10.1016/j.cag.2007.01.020	en
dc.identifier.volume	31	en
dc.identifier.issue	3	en
dc.identifier.spage	493	en
dc.identifier.epage	500	en