Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic

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dc.contributor.authorPavarino, Eduardo [UNESP]
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorNascimento, Marcelo Zanchetta do [UNESP]
dc.contributor.authorGodoy, Moacir Fernandes de
dc.contributor.authorArruda, Pedro Francisco de
dc.contributor.authorSanti Neto, Dalísio de
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionFaculdade de Medicina de São José do Rio Preto(FAMERP)
dc.contributor.institutionNúcleo Transdisciplinar para Estudo do Caos e da Complexidade (NUTECC)
dc.contributor.institutionHospital de Base de São José do Rio Preto
dc.date.accessioned2015-10-21T13:13:59Z
dc.date.available2015-10-21T13:13:59Z
dc.date.issued2015-01-01
dc.description.abstractIn this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.en
dc.description.affiliationUniversidade Federal de Uberlândia, Faculdade de Ciência da Computação
dc.description.affiliationHospital de Base de São José do Rio Preto, Departamento de Patologia
dc.description.affiliationUnespUniversidade Estadual Paulista, Departamento de Ciência da Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas de São José do Rio Preto
dc.format.extent1-4
dc.identifierhttp://iopscience.iop.org/article/10.1088/1742-6596/574/1/012135/meta
dc.identifier.citation3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014). Bristol: Iop Publishing Ltd, v. 574, p. 1-4, 2015.
dc.identifier.doi10.1088/1742-6596/574/1/012135
dc.identifier.fileWOS000352595600135.pdf
dc.identifier.issn1742-6588
dc.identifier.lattes2139053814879312
dc.identifier.urihttp://hdl.handle.net/11449/128818
dc.identifier.wosWOS:000352595600135
dc.language.isoeng
dc.publisherIop Publishing Ltd
dc.relation.ispartof3rd International Conference On Mathematical Modeling In Physical Sciences (IC-MSQUARE 2014)
dc.relation.ispartofsjr0,241
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.titleAutomatic method to classify images based on multiscale fractal descriptors and paraconsistent logicen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://iopscience.iop.org/page/copyright
dcterms.rightsHolderIop Publishing Ltd
unesp.author.lattes2139053814879312
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt

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Artigos - Ciência da Computação e Estatística - IBILCE