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hal.structure.identifier
dc.contributor.authorChen, Da*
hal.structure.identifier
dc.contributor.authorMirebeau, Jean-Marie
HAL ID: 5588
*
hal.structure.identifier
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
*
dc.date.accessioned2017-11-02T10:51:39Z
dc.date.available2017-11-02T10:51:39Z
dc.date.issued2016
dc.identifier.issn1748-3018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16891
dc.language.isoenen
dc.subjectGeodesic
dc.subjectminimal path
dc.subjectkeypoint
dc.subjecttubular structure extraction
dc.subjectpath score
dc.subjectretinal vessel segmentation
dc.subjectanisotropic fast marching
dc.subject.ddc519en
dc.titleVessel Tree Extraction using Radius-Lifted Keypoints Searching Scheme and Anisotropic Fast Marching Method
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenGeodesic methods have been widely applied to image analysis. They are particularly efficient to extract a tubular structure, such as a blood vessel, given its two endpoints in a 2D or 3D medical image. We address here a more difficult problem: the extraction of a full vessel tree structure given a single initial root point, by growing a collection of keypoints or new initial source points, connected by minimal geodesic paths. In this article, those keypoints are iteratively added, using a new detection criteria, which utilize the weighted geodesic distances with respect to a radius-lifted Riemannian metric, the standard Euclidean curve length and a path score. Two main weaknesses of classical keypoints searching approach are that the weighted geodesic distance and the Euclidean path length do not take into account the orientation of the tubular structure or object boundaries, due to the use of an isotropic geodesic Riemannian metric, and suffer from a leakage problem. In contrast, we use an anisotropic geodesic Riemannian metric, and develop new criteria for selecting keypoints based on the path score and automatically stopping the tree growth. Experimental results demonstrate that our method can obtain the expected results, which can extract vessel structures at a finer scale, with increased accuracy.
dc.relation.isversionofjnlnameJournal of Algorithms and Computational Technology
dc.relation.isversionofjnlvol10
dc.relation.isversionofjnlissue4
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages224-234
dc.relation.isversionofdoi10.1177/1748301816656289
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-12-12T14:44:11Z
hal.author.functionaut
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