dc.contributorGuerreiro, Ana Maria Guimarães
dc.contributor
dc.contributorhttp://lattes.cnpq.br/9970816105396107
dc.contributor
dc.contributorhttp://lattes.cnpq.br/8556144121380013
dc.contributorDória Neto, Adrião Duarte
dc.contributor
dc.contributorhttp://lattes.cnpq.br/1987295209521433
dc.contributorMartins, Allan De Medeiros
dc.contributor
dc.contributorhttp://lattes.cnpq.br/4402694969508077
dc.contributorLeite, Cicilia Raquel Maia
dc.contributor
dc.contributorhttp://lattes.cnpq.br/9378258073324535
dc.contributorCarvalho, Marco Antonio Garcia de
dc.contributor
dc.contributorhttp://lattes.cnpq.br/6366443994619479
dc.creatorVale, Alessandra Mendes Pacheco Guerra
dc.date.accessioned2016-01-21T19:07:52Z
dc.date.accessioned2022-10-06T13:36:22Z
dc.date.available2016-01-21T19:07:52Z
dc.date.available2022-10-06T13:36:22Z
dc.date.created2016-01-21T19:07:52Z
dc.date.issued2014-12-26
dc.identifierVALE, Alessandra Mendes Pacheco Guerra. Técnica para segmentação automática de imagens microscópicas de componentes sanguíneos e classificação diferencial de leucócitos baseada em lógica fuzzy. 2014. 100f. Tese (Doutorado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2014.
dc.identifierhttps://repositorio.ufrn.br/jspui/handle/123456789/19642
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3971078
dc.description.abstractAutomatic detection of blood components is an important topic in the field of hematology. The segmentation is an important stage because it allows components to be grouped into common areas and processed separately and leukocyte differential classification enables them to be analyzed separately. With the auto-segmentation and differential classification, this work is contributing to the analysis process of blood components by providing tools that reduce the manual labor and increasing its accuracy and efficiency. Using techniques of digital image processing associated with a generic and automatic fuzzy approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation of blood components and leukocyte differential classification, respectively, in microscopic images smears. Using the Fuzzy Inference System I, the proposed technique performs the segmentation of the image in four regions: the leukocyte’s nucleus and cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types: basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530 images containing microscopic samples of blood smears with different methods. The images were processed and its accuracy indices and Gold Standards were calculated and compared with the manual results and other results found at literature for the same problems. Regarding segmentation, a technique developed showed percentages of accuracy of 97.31% for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential classification, the percentage varied between 92.98% and 98.39% for the different leukocyte types. In addition to promoting auto-segmentation and differential classification, the proposed technique also contributes to the definition of new descriptors and the construction of an image database using various processes hematological staining
dc.languagepor
dc.publisherUniversidade Federal do Rio Grande do Norte
dc.publisherBrasil
dc.publisherUFRN
dc.publisherPROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO
dc.rightsAcesso Aberto
dc.subjectProcessamento digital de imagens
dc.subjectLógica fuzzy
dc.subjectSegmentação de imagens
dc.subjectClassificação diferencial de leucócitos
dc.subjectComponentes sanguíneos
dc.titleTécnica para segmentação automática de imagens microscópicas de componentes sanguíneos e classificação diferencial de leucócitos baseada em lógica fuzzy
dc.typedoctoralThesis


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