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Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering

2019-02Artigo científico Acesso restrito
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dc.contributor.authorPereira, Pedro M. M.
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorTavora, Luis M. N.
dc.contributor.authorAssunção, Pedro A. A.
dc.contributor.authorFaria, Sérgio M. M. de
dc.date.accessioned2026-03-17T18:41:39Z
dc.date.available2026-03-17T18:41:39Z
dc.date.issued2019-02
dc.description.abstractSegmentation is a key stage in dermoscopic image processing, where the accuracy of the border line that defines skin lesions is of utmost importance for subsequent algorithms (e.g., classification) and computer-aided early diagnosis of serious medical conditions. This paper proposes a novel segmentation method based on Local Binary Patterns (LBP), where LBP and K-Means clustering are combined to achieve a detailed delineation in dermoscopic images. In comparison with usual dermatologist-like segmentation (i.e., the available ground-truth), the proposed method is capable of finding more realistic borders of skin lesions, i.e., with much more detail. The results also exhibit reduced variability amongst different performance measures and they are consistent across different images. The proposed method can be applied for cell-based like segmentation adapted to the lesion border growing specificities. Hence, the method is suitable to follow the growth dynamics associated with the lesion border geometry in skin melanocytic images.eng
dc.description.sponsorshipThis work was supported by the Fundação para a Ciência e Tecnologia, Portugal, under PhD Grant SFRH/BD/128669/2017 and project PlenoISLA PTDC/EEI-TEL/28325/2017, in the scope of R&D Unit 50008, through national funds and where applicable co funded by FEDER – PT2020.
dc.identifier.citationPereira, Pedro & Fonseca-Pinto, Rui & Paiva, Rui Pedro & Tavora, Luis & Assunção, Pedro & De Faria, Sergio. (2019). Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering. 10.48550/arXiv.1902.06347.
dc.identifier.doi10.23919/MIPRO.2019.8757023
dc.identifier.issn2623-8764
dc.identifier.urihttp://hdl.handle.net/10400.8/15904
dc.language.isoeng
dc.peerreviewedyes
dc.publisherCornell University
dc.relationSkin Lesion Assessment based on Plenoptic Images for Melanoma Classification. Titulo anterior: Texture and Patterns in 3D plenoptic skin lesion assessment: A taxonomy proposal for melanoma classification
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/8757023/
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSegmentation
dc.subjectLesion Detection
dc.subjectMedical Imaging
dc.subjectDermoscopy
dc.titleAccurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clusteringeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberSFRH/BD/128669/2017
oaire.awardTitleSkin Lesion Assessment based on Plenoptic Images for Melanoma Classification. Titulo anterior: Texture and Patterns in 3D plenoptic skin lesion assessment: A taxonomy proposal for melanoma classification
oaire.awardURIhttp://hdl.handle.net/10400.8/14125
oaire.citation.endPage319
oaire.citation.startPage314
oaire.citation.titleInternational Convention on Information and Communication Technology, Electronics and Microelectronics
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamede Oliveira Pegado de Noronha E Távora
person.familyNameAssunção
person.familyNameFaria
person.givenNameLuís Miguel
person.givenNamePedro
person.givenNameSergio
person.identifier.ciencia-id121C-FADA-D750
person.identifier.ciencia-id6811-3984-C17B
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0002-8580-1979
person.identifier.orcid0000-0001-9539-8311
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridA-4827-2017
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id6701838347
person.identifier.scopus-author-id14027853900
relation.isAuthorOfPublication71940f24-f333-4ab6-abf6-00c7119a07c2
relation.isAuthorOfPublication25649bb9-f135-48e8-8d0f-3706b86701d3
relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublication.latestForDiscoveryf69bd4d6-a6ef-4d20-8148-575478909661
relation.isProjectOfPublication90b7c55a-b84e-4987-821f-cb7eb07499de
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