A stain color normalization with robust dictionary learning for breast cancer histological images processing

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dc.contributor.authorTosta, Thaína A. Azevedo
dc.contributor.authorFreitas, André Dias
dc.contributor.authorde Faria, Paulo Rogério
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorMartins, Alessandro Santana
dc.contributor.authordo Nascimento, Marcelo Zanchetta
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFederal Institute of Triângulo Mineiro
dc.date.accessioned2023-07-29T13:52:42Z
dc.date.available2023-07-29T13:52:42Z
dc.date.issued2023-08-01
dc.description.abstractMicroscopic analyses of tissue samples are crucial for confirming the diagnosis of breast cancer. The digitization of these samples has led to the development of computational systems that can assist pathologists. However, these systems may face limitations owing to color variations in the images. Normalization studies have been widely conducted to address these issues, but there is still a need for new proposals that take into account the biological properties of dyes and tissues. This study presents a novel method for normalizing hematoxylin and eosin-stained histological images by estimating the color appearance matrices and density maps of the stain. The proposed method offers contributions in terms of pixel selection and weight definition to improve the color estimation of histological images. Besides, to the best of our knowledge, no previous studies have evaluated normalized images considering both handcrafted and learning features. Breast cancer images with significant color variations were used to evaluate this approach and the results demonstrated its effectiveness and efficiency. The average values of FSIM, NIQE, and QSSIM were up to 0.9866, 3.4298, and 0.9655, respectively. Compared with other normalization techniques, the proposed method showed an increase of up to 5.9261, with the largest difference observed in the amount of noise added, as indicated by the NIQE metric. To determine the impact of normalization on feature extraction, the evaluations included an analysis of both color and deep-learned features. These experiments showed that all evaluated methods harmed the separation of breast cancer samples by color features. In contrast, the deep-learned features resulted in less complex classification problems, especially with the proposed normalization. This technique also reached one of the lowest processing times, nearly 6 s with the largest image from the databases.en
dc.description.affiliationInstitute of Science and Technology Federal University of São Paulo, Av. Cesare Mansueto Giulio Lattes, 1201, São Paulo
dc.description.affiliationDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia, Av. Amazonas, S/N, Minas Gerais
dc.description.affiliationDepartment of Computer Science and Statistics São Paulo State University, R. Cristóvão Colombo, 2265, São José do Rio PretoSão Paulo
dc.description.affiliationFederal Institute of Triângulo Mineiro, R. Belarmino Vilela Junqueira S/N, Minas Gerais
dc.description.affiliationFaculty of Computer Science Federal University of Uberlândia, Av. João Naves de Ávila, 2121, Minas Gerais
dc.description.affiliationUnespDepartment of Computer Science and Statistics São Paulo State University, R. Cristóvão Colombo, 2265, São José do Rio PretoSão Paulo
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipIdCAPES: #1575210
dc.description.sponsorshipIdFAPESP: #2022/03020-1
dc.description.sponsorshipIdFAPEMIG: #APQ-00578-18)
dc.description.sponsorshipIdCAPES: 001
dc.identifierhttp://dx.doi.org/10.1016/j.bspc.2023.104978
dc.identifier.citationBiomedical Signal Processing and Control, v. 85.
dc.identifier.doi10.1016/j.bspc.2023.104978
dc.identifier.issn1746-8108
dc.identifier.issn1746-8094
dc.identifier.scopus2-s2.0-85153849644
dc.identifier.urihttp://hdl.handle.net/11449/248749
dc.language.isoeng
dc.relation.ispartofBiomedical Signal Processing and Control
dc.sourceScopus
dc.subjectColor normalization
dc.subjectDictionary learning
dc.subjectFeatures analysis
dc.subjectH&E histological images analysis
dc.subjectSparse non-negative matrix factorization
dc.titleA stain color normalization with robust dictionary learning for breast cancer histological images processingen
dc.typeArtigo
unesp.author.orcid0000-0002-9291-8892[1]
unesp.author.orcid0000-0003-2944-1646[2]
unesp.author.orcid0000-0003-2650-3960[3]
unesp.author.orcid0000-0003-4635-5037[5]

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