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dc.contributor.authorDeschamps, Thomas
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.date.accessioned2012-05-15T14:11:14Z
dc.date.available2012-05-15T14:11:14Z
dc.date.issued2007
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9220
dc.language.isoenen
dc.subjectVirtual endoscopyen
dc.subject3D medical imagingen
dc.subjectSkeletonizationen
dc.subjectMinimal pathsen
dc.subjectFast-marchingen
dc.subjectSegmentationen
dc.subject.ddc006.3en
dc.titleSegmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imagingen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherMedical Imaging Systems group - Philips Recherche France & Laboratoire CEREMADE-CNRS;France
dc.description.abstractenWe present a new fast approach for segmentation of thin branching structures, like vascular trees, based on Fast-Marching (FM) and Level Set (LS) methods. FM allows segmentation of tubular structures by inflating a “long balloon” from a user given single point. However, when the tubular shape is rather long, the front propagation may blow up through the boundary of the desired shape close to the starting point. Our contribution is focused on a method to propagate only the useful part of the front while freezing the rest of it. We demonstrate its ability to segment quickly and accurately tubular and tree-like structures. We also develop a useful stopping criterion for the causal front propagation. We finally derive an efficient algorithm for extracting an underlying 1D skeleton of the branching objects, with minimal path techniques. Each branch being represented by its centerline, we automatically detect the bifurcations, leading to the “Minimal Tree” representation. This so-called “Minimal Tree” is very useful for visualization and quantification of the pathologies in our anatomical data sets. We illustrate our algorithms by applying it to several arteries datasets.en
dc.relation.isversionofjnlnameComputer Methods in Biomechanics and Biomedical Engineering
dc.relation.isversionofjnlvol10en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2007
dc.relation.isversionofjnlpages289-305en
dc.relation.isversionofdoihttp://dx.doi.org/10.1080/10255840701328239en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherTaylor and Francisen
dc.subject.ddclabelIntelligence artificielleen


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