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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro-
Autor(es): dc.creatorTosta, Thaína A. Azevedo-
Autor(es): dc.creatorFreitas, André Dias-
Autor(es): dc.creatorde Faria, Paulo Rogério-
Autor(es): dc.creatorNeves, Leandro Alves-
Autor(es): dc.creatorMartins, Alessandro Santana-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Data de aceite: dc.date.accessioned2025-08-21T16:14:22Z-
Data de disponibilização: dc.date.available2025-08-21T16:14:22Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.bspc.2023.104978-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248749-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248749-
Descrição: dc.descriptionMicroscopic 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)-
Descrição: dc.descriptionInstitute of Science and Technology Federal University of São Paulo, Av. Cesare Mansueto Giulio Lattes, 1201, São Paulo-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia, Av. Amazonas, S/N, Minas Gerais-
Descrição: dc.descriptionDepartment of Computer Science and Statistics São Paulo State University, R. Cristóvão Colombo, 2265, São José do Rio PretoSão Paulo-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro, R. Belarmino Vilela Junqueira S/N, Minas Gerais-
Descrição: dc.descriptionFaculty of Computer Science Federal University of Uberlândia, Av. João Naves de Ávila, 2121, Minas Gerais-
Descrição: dc.descriptionDepartment of Computer Science and Statistics São Paulo State University, R. Cristóvão Colombo, 2265, São José do Rio PretoSão Paulo-
Descrição: dc.descriptionCAPES: #1575210-
Descrição: dc.descriptionFAPESP: #2022/03020-1-
Descrição: dc.descriptionFAPEMIG: #APQ-00578-18)-
Descrição: dc.descriptionCAPES: 001-
Idioma: dc.languageen-
Relação: dc.relationBiomedical Signal Processing and Control-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectColor normalization-
Palavras-chave: dc.subjectDictionary learning-
Palavras-chave: dc.subjectFeatures analysis-
Palavras-chave: dc.subjectH&E histological images analysis-
Palavras-chave: dc.subjectSparse non-negative matrix factorization-
Título: dc.titleA stain color normalization with robust dictionary learning for breast cancer histological images processing-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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