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Fast Surface Segmentation Guided by User Input Using Implicit Extension of Minimal Paths

dc.contributor.authorArdon, Roberto
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorYezzi, Anthony
dc.date.accessioned2014-08-26T13:29:40Z
dc.date.available2014-08-26T13:29:40Z
dc.date.issued2006
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13816
dc.language.isoenen
dc.subjectimage segmentationen
dc.subjectactive contoursen
dc.subjectminimal pathsen
dc.subjectlevel set methoden
dc.subjectobject extractionen
dc.subjectstationary transport equationen
dc.subject.ddc006.3en
dc.titleFast Surface Segmentation Guided by User Input Using Implicit Extension of Minimal Pathsen
dc.typeArticle accepté pour publication ou publié
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 or more known curves (the constraining curves), given by an expert. The aim of our method is to find the desired surface by exploiting the information given in the supplied curves as much as possible. We use a cost potential which penalizes image regions of low interest (for example areas of low gradient). 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. These paths globally minimize an energy which is defined from the cost potential. Our approach, although conceptually different, can be seen as an implicit extension to 3D of the minimal path framework already known for 2D image segmentation. As for this previous approach, and unlike other variational methods, our method is not prone to local minima traps of the energy. We present a fast implementation which has been successfully applied to 3D medical and synthetic images.en
dc.relation.isversionofjnlnameJournal of Mathematical Imaging and Vision
dc.relation.isversionofjnlvol25en
dc.relation.isversionofjnlissue3en
dc.relation.isversionofjnldate2006
dc.relation.isversionofjnlpages289-305en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s10851-006-9641-9en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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