Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means

Stoitsis, J; Golemati, S; Nikita, KS; Nicolaides, AN

dc.contributor.author	Stoitsis, J	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Nicolaides, AN	en
dc.date.accessioned	2014-03-01T02:42:32Z
dc.date.available	2014-03-01T02:42:32Z
dc.date.issued	2004	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31033
dc.subject	Carotid atherosclerosis	en
dc.subject	Fuzzy c-means	en
dc.subject	Motion	en
dc.subject	Texture	en
dc.subject	Ultrasound	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Diseases	en
dc.subject.other	Feature extraction	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Image processing	en
dc.subject.other	Medical imaging	en
dc.subject.other	Statistical methods	en
dc.subject.other	Textures	en
dc.subject.other	Visualization	en
dc.subject.other	Cartoid atherosclerosis	en
dc.subject.other	Fuzzy c-means	en
dc.subject.other	Maximal surface velocity (MSV)	en
dc.subject.other	Motion patterns	en
dc.subject.other	Ultrasonic imaging	en
dc.title	Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2004.1403437	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2004.1403437	en
heal.publicationDate	2004	en
heal.abstract	Analysis of B-mode ultrasound images of the carotid atheromatous plaque includes the estimation of texture from static images and the estimation of motion from image sequences. The combination of these two types of information may be valuable for accurate diagnosis of vascular disease. The purpose of this paper was to study texture and motion patterns of carotid atherosclerosis and select the optimal combination of features that can characterize plaque. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 99 texture features were estimated using first-order statistics, second-order statistics, Laws texture energy and the fractal dimension. Only five texture features were significantly different between the two groups. In the same subjects, the motion of selected plaque regions was estimated using region tracking and block-matching and expressed through: a/maximal surface velocity (MSV), and b/maximal relative surface velocity (MRSV). MSV and MRSV were significantly lower in asymptomatic plaques suggesting more homogeneous motion patterns. Clustering using fuzzy c-means correctly classified 74% of plaques based on texture features only, and 79% of plaques based on motion features only. Classification performance reached 84% when a combination of motion and texture features was used.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2004.1403437	en
dc.identifier.volume	26 II	en
dc.identifier.spage	1407	en
dc.identifier.epage	1410	en