Classification of breast and colorectal tumors based on percolation of color normalized images

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dc.contributor.authorRoberto, Guilherme F.
dc.contributor.authorNascimento, Marcelo Z.
dc.contributor.authorMartins, Alessandro S.
dc.contributor.authorTosta, Thaína A.A.
dc.contributor.authorFaria, Paulo R.
dc.contributor.authorNeves, Leandro A. [UNESP]
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionFederal Institute of Triângulo Mineiro (IFTM)
dc.contributor.institutionUniversidade Federal do ABC (UFABC)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-12T01:39:57Z
dc.date.available2020-12-12T01:39:57Z
dc.date.issued2019-11-01
dc.description.abstractPercolation is a fractal descriptor that has been applied recently on computer vision problems. We applied this descriptor on 58 colored histological breast images, and 165 colored histological colorectal images, both stained with Hematoxylin and Eosin, in order to extract features to differentiate between benign and malignant cases. The experiments were also performed over normalized images, aiming to analyze the influence of different color normalization techniques on percolation-based features and whether they can provide better classification results. The feature sets obtained from the application of the method on the original images and on the normalized images with three different techniques were tested using 12 different classifiers. We compared the obtained results with other relevant methods in the area and observed significant contributions, with AUC rates above 0.900 in both normalized and non-normalized images. We also verified that color normalization does not contribute to the classification of breast tumors when associated with percolation features. However, color normalized images from the colorectal tumor's dataset provided better results than the original images.en
dc.description.affiliationFaculty of Computation (FACOM) Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB
dc.description.affiliationFederal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N
dc.description.affiliationCenter of Mathematics Computing and Cognition Federal University of ABC (UFABC), Av. dos Estados, 5001
dc.description.affiliationDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N
dc.description.affiliationDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265
dc.description.affiliationUnespDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdCNPq: #304848/2018-2
dc.description.sponsorshipIdCNPq: #313365/2018-0
dc.description.sponsorshipIdCNPq: #427114/2016-0
dc.description.sponsorshipIdCNPq: #430965/2018-4
dc.description.sponsorshipIdFAPEMIG: #APQ-00578-18
dc.description.sponsorshipIdCAPES: 001
dc.format.extent134-143
dc.identifierhttp://dx.doi.org/10.1016/j.cag.2019.08.008
dc.identifier.citationComputers and Graphics (Pergamon), v. 84, p. 134-143.
dc.identifier.doi10.1016/j.cag.2019.08.008
dc.identifier.issn0097-8493
dc.identifier.scopus2-s2.0-85072573634
dc.identifier.urihttp://hdl.handle.net/11449/199448
dc.language.isoeng
dc.relation.ispartofComputers and Graphics (Pergamon)
dc.sourceScopus
dc.subjectBreast tumors
dc.subjectColor normalization
dc.subjectColorectal tumors
dc.subjectImage classification
dc.subjectPercolation
dc.titleClassification of breast and colorectal tumors based on percolation of color normalized imagesen
dc.typeArtigo
unesp.author.orcid0000-0002-1495-0037[1]
unesp.author.orcid0000-0003-3537-0178[2]
unesp.author.orcid0000-0001-8580-7054[6]

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