A New interactive method for coronary arteries segmentation based on tubular anisotropy
dc.contributor.author | Cohen, Laurent D.
HAL ID: 738939 | |
dc.contributor.author | Benmansour, Fethallah | |
dc.date.accessioned | 2012-06-11T13:34:49Z | |
dc.date.available | 2012-06-11T13:34:49Z | |
dc.date.issued | 2009 | |
dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/9421 | |
dc.language.iso | en | en |
dc.subject | Minimal path method | en |
dc.subject | 3D medical images | en |
dc.subject | tubular structure | en |
dc.subject | anisotropy | en |
dc.subject | segmenting images 3D | en |
dc.subject.ddc | 006.3 | en |
dc.title | A New interactive method for coronary arteries segmentation based on tubular anisotropy | en |
dc.type | Communication / Conférence | |
dc.description.abstracten | In this paper we present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 3D medical images. The basic tools are minimal paths solved using the fast marching algorithm. This leads to interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize (based on the local orientation using a Riemannian Metric). This approach is made available for the physician using an Object Oriented Language (OOL) interface. We show results on two CT cardiac images for coronary arteries segmentation. | en |
dc.identifier.citationpages | 41-44 | en |
dc.relation.ispartoftitle | Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on | en |
dc.relation.ispartofpublname | IEEE | en |
dc.relation.ispartofdate | 2009 | |
dc.description.sponsorshipprivate | oui | en |
dc.subject.ddclabel | Intelligence artificielle | en |
dc.relation.ispartofisbn | E-ISBN : 978-1-4244-3932-4 Print ISBN: 978-1-4244-3931-7 | en |
dc.relation.conftitle | ISBI 2009 | en |
dc.relation.confdate | 2009-07 | |
dc.relation.confcity | Boston | en |
dc.relation.confcountry | États-Unis | en |
dc.identifier.doi | http://dx.doi.org/10.1109/ISBI.2009.5192978 |
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