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dc.contributor.authorBenmansour, Fethallah
dc.contributor.authorCohen, Laurent D.
dc.date.accessioned2011-04-26T12:25:52Z
dc.date.available2011-04-26T12:25:52Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6043
dc.language.isoenen
dc.subjectVessel segmentationen
dc.subjectMinimal path methoden
dc.subjectFast marching algorithmen
dc.subjectAnisotropyen
dc.subjectEnhancementen
dc.subjectMulti-scaleen
dc.subject.ddc519en
dc.titleTubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancementen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are minimal paths solved using the fast marching algorithm. This allows 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. This is done 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. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric. The designed metric is well oriented along the direction of the vessel, admits higher velocity on the centerline, and provides a good estimate of the vessel radius. Based on the optimally oriented flux this measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. We obtain promising results on noisy synthetic and real 2D and 3D images and we present a clinical validation.en
dc.relation.isversionofjnlnameInternational Journal of Computer Vision
dc.relation.isversionofjnlvol92en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2011
dc.relation.isversionofjnlpages192-210en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s11263-010-0331-0en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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