MR functional cardiac imaging: Segmentation, measurement and WWW based visualization of 4D data

Delibasis, KK; Mouravliansky, N; Matsopoulos, GK; Nikita, KS; Marsh, A

dc.contributor.author	Delibasis, KK	en
dc.contributor.author	Mouravliansky, N	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Marsh, A	en
dc.date.accessioned	2014-03-01T01:14:51Z
dc.date.available	2014-03-01T01:14:51Z
dc.date.issued	1999	en
dc.identifier.issn	0167-739X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13247
dc.subject	4D MR cardiac images	en
dc.subject	K-means clustering algorithm	en
dc.subject	fuzzy K-means (FKM) algorithm	en
dc.subject	self-organizing maps (SOMs)	en
dc.subject	seeded region growing algorithm	en
dc.subject	sctive surface	en
dc.subject	surface triangulation	en
dc.subject	VRML 2.0 format	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Algorithms	en
dc.subject.other	Computational geometry	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Image analysis	en
dc.subject.other	Image quality	en
dc.subject.other	Image segmentation	en
dc.subject.other	Virtual reality	en
dc.subject.other	Visualization	en
dc.subject.other	World Wide Web	en
dc.subject.other	Magnetic resonance functional cardiac imaging	en
dc.subject.other	Seeded region growing algorithms	en
dc.subject.other	Self-organizing maps (SOM)	en
dc.subject.other	Magnetic resonance imaging	en
dc.title	MR functional cardiac imaging: Segmentation, measurement and WWW based visualization of 4D data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0167-739X(98)00062-4	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0167-739X(98)00062-4	en
heal.language	English	en
heal.publicationDate	1999	en
heal.abstract	This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different methods are evaluated for the process of segmentation of the objects of interest: The K-means clustering algorithm, the fuzzy K-means (FKM) algorithm, self-organizing maps (SOMs) and seeded region growing algorithm. The technique of active surface is then subsequently applied to refine the segmentation results, employing a deformable generalised cylinder as geometric primitive. The final ventricular models are presented in VRML 2.0 format. The same process is repeated for all the 3D volumes of the cardiac cycle. The radial displacement between end systole and end diastole is calculated for each point of the active surface and is encoded in colour on the VRML vertex, using the RGB colour model. Using the VRML 2.0 specifications, morphing is performed showing all cardiac phases in real time. The expert has the ability to view the objects and interact with them using a simple internet browser. Preliminary results of normal and abnormal cases indicate that very important pathological situations (such as infarction) can be visualised and thus easily diagnosed and localised with the assistance of the proposed technique. (C) 1999 Elsevier Science B.V. All rights reserved.	en
heal.publisher	Elsevier Science Publishers B.V.	en
heal.journalName	Future Generation Computer Systems	en
dc.identifier.doi	10.1016/S0167-739X(98)00062-4	en
dc.identifier.isi	ISI:000079390300005	en
dc.identifier.volume	15	en
dc.identifier.issue	2	en
dc.identifier.spage	185	en
dc.identifier.epage	193	en