A computational approach for detecting pigmented skin lesions in macroscopic images

dc.contributor.authorOliveira, Roberta B.
dc.contributor.authorMarranghello, Norian [UNESP]
dc.contributor.authorPereira, Aledir S. [UNESP]
dc.contributor.authorTavares, Joao Manuel R. S.
dc.contributor.institutionUniv Porto
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:48:21Z
dc.date.available2018-11-26T16:48:21Z
dc.date.issued2016-11-01
dc.description.abstractSkin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising. (C) 2016 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Porto, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Dept Engn Mecan, Fac Engn, Rua Dr Roberto Frias,S-N, P-4200465 Oporto, Portugal
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Comp & Estat, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Comp & Estat, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipSciTech - Science and Technology for Competitive and Sustainable Industries
dc.description.sponsorshipPrograma Operacional Regional do Norte (NORTE), through Fundo Europeu de Desenvolvimento Regional (FEDER)
dc.description.sponsorshipIdSciTech - Science and Technology for Competitive and Sustainable Industries: NORTE-01-0145-FEDER-000022
dc.format.extent53-63
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2016.05.017
dc.identifier.citationExpert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 61, p. 53-63, 2016.
dc.identifier.doi10.1016/j.eswa.2016.05.017
dc.identifier.fileWOS000379634700005.pdf
dc.identifier.issn0957-4174
dc.identifier.lattes2098623262892719
dc.identifier.orcid0000-0003-1086-3312
dc.identifier.urihttp://hdl.handle.net/11449/161717
dc.identifier.wosWOS:000379634700005
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofExpert Systems With Applications
dc.relation.ispartofsjr1,271
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectImage pre-processing
dc.subjectImage segmentation
dc.subjectImage classification
dc.subjectAnisotropic diffusion filter
dc.subjectActive contour model without edges
dc.subjectSupport vector machine
dc.titleA computational approach for detecting pigmented skin lesions in macroscopic imagesen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes2098623262892719[2]
unesp.author.orcid0000-0003-1086-3312[2]

Arquivos

Pacote Original
Agora exibindo 1 - 1 de 1
Carregando...
Imagem de Miniatura
Nome:
WOS000379634700005.pdf
Tamanho:
1.74 MB
Formato:
Adobe Portable Document Format
Descrição: