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Vessel Extraction Using Crossing-Adaptive Minimal Path Model with Anisotropic Enhancement and Curvature Constraint

hal.structure.identifier
dc.contributor.authorLiu, Li
HAL ID: 17735
ORCID: 0000-0002-4497-0135
hal.structure.identifier
dc.contributor.authorChen, Da
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
hal.structure.identifierCentre de Recherche en Information Biomédicale sino-français [CRIBS]
dc.contributor.authorHuazhong, Shu
hal.structure.identifierInstitut de la Vision
dc.contributor.authorPâques, Michel
dc.date.accessioned2019-10-12T14:16:52Z
dc.date.available2019-10-12T14:16:52Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20117
dc.language.isoenen
dc.subjectGeodesicen
dc.subjectanisotropy enhancementen
dc.subjectRiemannian metricen
dc.subjectpath featureen
dc.subjecttubular structure segmentationen
dc.subject.ddc621.3en
dc.titleVessel Extraction Using Crossing-Adaptive Minimal Path Model with Anisotropic Enhancement and Curvature Constrainten
dc.typeCommunication / Conférence
dc.description.abstractenIn this work, we propose a new minimal path model with a dynamic Riemannian metric to overcome the shortcuts problem in vessel extraction. The invoked metric consists of a crossing-adaptive anisotropic radius-lifted tensor field and a front freezing indicator. It is able to reduce the anisotropy of the metric on the crossing points and steer the front evolution by freezing the points causing high curvature of a geodesic. We validate our model on the DRIVE and IOSTAR datasets, and the segmentation accuracy is 0.861 and 0.881, respectively. The proposed method can extract the centreline position and vessel width efficiently and accuracy.en
dc.relation.ispartoftitle2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)en
dc.relation.ispartofpublnameIEEE - Institute of Electrical and Electronics Engineersen
dc.relation.ispartofpublcityPiscataway, NJen
dc.relation.ispartofdate2019-04
dc.subject.ddclabelTraitement du signalen
dc.relation.conftitle2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)en
dc.relation.confdate2019-04
dc.relation.confcityVeniseen
dc.relation.confcountryItalyen
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/ISBI.2019.8759435en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-10-12T14:13:31Z
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