DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO

URI Permanente para esta coleçãohttps://tedebc-teste.ufma.br/handle/tede/1314

Áreas de Concentração e Linhas de Pesquisa: Automação e Contrôle Ciência da Computação Sistemas de Energia Elétrica

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Navegando DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO por Autor "AIRES, Kelson Romulo Teixeira"

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    Diagnóstico de glaucoma a partir de imagens de fundo de olho utilizando índices de diversidade
    (Universidade Federal do Maranhão, 2018-02-26) ARAUJO, Jose Denes Lima; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; BRAZ JUNIOR, Geraldo; http://lattes.cnpq.br/8287861610873629; AIRES, Kelson Romulo Teixeira; http://lattes.cnpq.br/0065931835203045
    Glaucoma is one of the leading causes of blindness worldwide, and is usually caused by an increase in the intraocular pressure that damages the optic nerve and gradually causes vision loss. It is a disease that has no symptoms in the early stages and its early diagnosis can prevent the vision loss and blindness. Fundus images are used by experts to examine the optic disc in order to identify the changes caused by glaucoma. In addition, image processing and pattern recognition techniques are used for the development of computational tools in order to provide support in the process of analyzing these images. This work proposes a methodology for the glaucoma diagnosis from fundus images using diversity indexes as texture descriptors. After extraction of texture features, genetic algorithms are used to select the best set of features. Finally, the support vector machine is used to perform the classification. The proposed methodology revealed promising results for glaucoma diagnosis, reaching accuracy of 93.41%, sensitivity of 92.36% and specificity of 95.05%, as best results