Individual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networks

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dc.contributor.authorKim, Seongeunko
dc.contributor.authorLee, Chang-Ockko
dc.date.accessioned2023-12-11T08:00:10Z-
dc.date.available2023-12-11T08:00:10Z-
dc.date.created2023-12-11-
dc.date.issued2024-01-
dc.identifier.citationSIGNAL PROCESSING-IMAGE COMMUNICATION, v.120-
dc.identifier.issn0923-5965-
dc.identifier.urihttp://hdl.handle.net/10203/316242-
dc.description.abstractIn human teeth images taken outside the oral cavity with a general optical camera, it is difficult to segment individual tooth due to common obstacles such as weak edges, intensity inhomogeneities and strong light reflections. In this work, we propose a method for segmenting individual tooth in human teeth images. The key to this method is to obtain pseudo edge-region using deep neural networks. After an additional step to obtain initial contours for each tooth region, the individual tooth is segmented by applying active contour models. We also present a strategy using existing model-based methods for labeling the data required for neural network training.-
dc.languageEnglish-
dc.publisherELSEVIER-
dc.titleIndividual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networks-
dc.typeArticle-
dc.identifier.wosid001108415900001-
dc.identifier.scopusid2-s2.0-85175246824-
dc.type.rimsART-
dc.citation.volume120-
dc.citation.publicationnameSIGNAL PROCESSING-IMAGE COMMUNICATION-
dc.identifier.doi10.1016/j.image.2023.117076-
dc.contributor.localauthorLee, Chang-Ock-
dc.contributor.nonIdAuthorKim, Seongeun-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorTooth segmentation-
dc.subject.keywordAuthorNeural network-
dc.subject.keywordAuthorGeometric attraction-driven flow-
dc.subject.keywordAuthorEdge-region-
dc.subject.keywordAuthorLight reflection-
dc.subject.keywordPlusLEVEL SET METHOD-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusFLOW-
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