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dc.contributor.authorAzevedo Tosta, Thaina A.
dc.contributor.authorFaria, Paulo Rogerio de
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorNascimento, Marcelo Zanchetta do
dc.identifier.citationArtificial Intelligence In Medicine. Amsterdam: Elsevier Science Bv, v. 95, p. 118-132, 2019.
dc.description.abstractDifferent types of cancer can be diagnosed with the analysis of histological samples stained with hematoxylin-eosin (H&E). Through this stain, it is possible to identify the architecture of tissue components and analyze cellular morphological aspects that are essential for cancer diagnosis. However, preparation and digitization of histological samples can lead to color variations that influence the performance of segmentation and classification algorithms in histological image analysis systems. Among the determinant factors of these color variations are different staining time, concentration and pH of the solutions, and the use of different digitization systems. This has motivated the development of normalization algorithms of histological images for their color adjustments. These methods are designed to guarantee that biological samples are not altered and artifacts are not introduced in the images, thus compromising the lesions diagnosis. In this context, normalization techniques are proposed to minimize color variations in histological images, and they are topics covered by important studies in the literature. In this proposal, it is presented a detailed study of the state of art of computational normalization of H&E-stained histological images, highlighting the main contributions and limitations of correlated works. Besides, the evaluation of normalization methods published in the literature are depicted and possible directions for new methods are described.en
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.publisherElsevier B.V.
dc.relation.ispartofArtificial Intelligence In Medicine
dc.sourceWeb of Science
dc.subjectHistological image analysis
dc.subjectColor corrections
dc.titleComputational normalization of H&E-stained histological images: Progress, challenges and future potentialen
dcterms.rightsHolderElsevier B.V.
dc.contributor.institutionFed Univ ABC
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationFed Univ ABC, Ctr Math Comp & Cognit, Ave Estados 5001, BR-09210580 Sao Paulo, Brazil
dc.description.affiliationUniv Fed Uberlandia, Dept Histol & Morphol, Inst Biomed Sci, Ave Amazonas S-N, BR-38405320 Uberlandia, MG, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, Sao Paulo, Brazil
dc.description.affiliationUniv Fed Uberlandia, Fac Comp Sci, Ave Joao Naves de Avila 2121, BR-38400902 Uberlandia, MG, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, Sao Paulo, Brazil
dc.rights.accessRightsAcesso aberto
dc.description.sponsorshipIdCAPES: 1575210
dc.description.sponsorshipIdFAPEMIG: TEC - APQ-02885-15
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