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dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
dc.contributor.authorRouchdy, Youssef
dc.date.accessioned2013-07-02T10:49:49Z
dc.date.available2013-07-02T10:49:49Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11502
dc.language.isoenen
dc.subjectTree Structure Segmentationen
dc.subjectMinimal Pathsen
dc.subjectLevel Seten
dc.subjectFast Marchingen
dc.subjectGeodesic Votingen
dc.subject.ddc006.3en
dc.titleGeodesic voting for the automatic extraction of tree structures. Methods and applicationsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper presents new methods to segment thin tree structures, which are, for example present in microglia extensions and cardiac or neuronal blood vessels. Many authors have used minimal cost paths, or geodesics relative to a local weighting potential P, to find a vessel pathway between two end points. We utilize a set of such geodesic paths to find a tubular tree structure by seeking minimal interaction. We introduce a new idea that we call Geodesic Voting or Geodesic Density. The approach consists of computing geodesics from a set of end points scattered in the image which flow toward a given source point. The target structure corresponds to image points with a high geodesic density. The ”Geodesic density” is defined at each pixel of the image as the number of geodesics that pass over this pixel. The potential P is defined in such way that it takes low values along the tree structure, therefore geodesics will migrate toward this structure thereby yielding a high geodesic density. We further adapt these methods to segment complex tree structures in a noisy medium and apply them to segment microglia extensions from confocal microscope images as well as vessels.en
dc.relation.isversionofjnlnameComputer Vision and Image Understanding
dc.relation.isversionofjnlnameComputer Vision and Image Understanding
dc.relation.isversionofjnlvol117
dc.relation.isversionofjnlissue10
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages1453–1467
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.cviu.2013.06.001en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelIntelligence artificielleen


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