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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    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
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    Segmentação automática da próstata em imagens de ressonância magnética utilizando redes neurais convolucionais, mapa probabilístico e treinamento adversário
    (Universidade Federal do Maranhão, 2019-02-15) FERREIRA, Jonnison Lima; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; CAVALCANTE, André Borges; http://lattes.cnpq.br/3885279033465023; BRAZ JÚNIOR, Geraldo; http://lattes.cnpq.br/8287861610873629; CARVALHO FILHO, Antonio Oseas de; http://lattes.cnpq.br/7913655222849728
    Prostate cancer is the second most common cancer among men, being the second most deadly. Early detection is a strategy to find the tumor at an early stage and thus provide a better chance of treatment. Currently the prostate gland imaging test has grown for prevention, diagnosis and treatment. The manual segmentation of the prostate is delayed and the propensity to variability among those expected, due to work, alternatives such as computational systems that use image processing and the identification of more advanced and exploited patterns for the early diagnosis of this disease, providing a second opinion for the specialist and increase the process. In this work, several automatic tasks are provided for the segmentation of the prostate from magnetic resonance imaging using a deep learning technique, probabilistic mapping and adversarial training of neural networks. The proposed methodology was tested on two public imaging databases, the Prostate 3T prostate and the PROMISE12, resulting in an average Dice of 89%.