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dc.contributor.authorArdon, Roberto
dc.contributor.authorCohen, Laurent D.
dc.contributor.authorYezzi, Anthony
dc.date.accessioned2012-05-15T14:49:34Z
dc.date.available2012-05-15T14:49:34Z
dc.date.issued2007
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9222
dc.language.isoenen
dc.subjectImage segmentationen
dc.subjectMinimal Pathsen
dc.subjectActive contours,en
dc.subjectLevel set methoden
dc.subjectStationary transport equation.en
dc.subject.ddc006.3en
dc.titleA New Implicit Method for Surface Segmentation by Minimal Paths in 3D Imagesen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherMedisysResearch Lab (Medisys) Philips Healthcare;France
dc.contributor.editoruniversityotherGeorgia Institute of Technology (GATECH) http://www.gatech.edu/ Georgia Institute of Technology;États-Unis
dc.description.abstractenWe introduce a novel implicit approach for single-object segmentation in 3D images. The boundary surface of this object is assumed to contain two known curves (the constraining curves), given by an expert. The aim of our method is to find the wanted surface by exploiting as much as possible the information given in the supplied curves and in the image. As for active surfaces, we use a cost potential that penalizes image regions of low interest (most likely areas of low gradient or too far from the surface to be extracted). In order to avoid local minima, we introduce a new partial differential equation and use its solution for segmentation. We show that the zero level set of this solution contains the constraining curves as well as a set of paths joining them. We present a fast implementation that has been successfully applied to 3D medical and synthetic images.en
dc.relation.isversionofjnlnameApplied Mathematics and Optimization
dc.relation.isversionofjnlvol55en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2007
dc.relation.isversionofjnlpages127-144en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s00245-006-0885-yen
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelIntelligence artificielleen


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