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Dental image segmentation by clustering methods

dc.contributor.authorBalsa, Carlos
dc.contributor.authorAlves, Cláudio
dc.contributor.authorGuivarch, Ronan
dc.contributor.authorMouysset, Sandrine
dc.date.accessioned2023-02-28T12:00:53Z
dc.date.available2023-02-28T12:00:53Z
dc.date.issued2021
dc.description.abstractSegmentation of dental radiography allows the identification of human individuals but also could be used for the development of more effective diagnostic, monitoring, and evaluation of appropriate treatment plans. In practice, dark background and bones tissues are not distinguished with contour extraction methods on dental images. So we propose to first apply the k-means method and then extract the contours on the clustering result. We present an initialization of the k centroids based on the grey scale histograms, a weighted norm that includes both grey scale and geometrical information, and tests it on dental X-ray images. Then we describe a promising parallel clustering method based on kernel affinity.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBalsa, Carlos; Alves, Cláudio; Guivarch, Ronan; Mouysset, Sandrine (2021). Dental image segmentation by clustering methods. In 1st International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021.pt_PT
dc.identifier.doi10.1007/978-3-030-90241-4_1pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27292
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectImage segmentationpt_PT
dc.subjectDental radiographypt_PT
dc.subjectk-meanspt_PT
dc.subjectNormspt_PT
dc.subjectSpectral clusteringpt_PT
dc.titleDental image segmentation by clustering methodspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage17pt_PT
oaire.citation.startPage3pt_PT
oaire.citation.title1st International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021pt_PT
oaire.citation.volume1485pt_PT
person.familyNameBalsa
person.givenNameCarlos
person.identifier1721518
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.orcid0000-0003-2431-8665
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id23391719100
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d

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