Diagnóstico de câncer de mama a partir de imagens de mamografia 2d utilizando descritores de forma 3d

• Ano de defesa: 2018
• Autor(a) principal: SOUZA, Johnatan Carvalho
• Orientador(a): PAIVA, Anselmo Cardoso de
• Banca de defesa: PAIVA, Anselmo Cardoso de , SILVA, Aristófanes Corrêa , SANTANA, Ewaldo Eder Carvalho , CARVALHO FILHO, Antonio Oseas de , ROCHA, Simara Vieira da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Diagnóstico de câncer de mama; Mamografia; Distribuição de forma; Descritores de forma dentários; Descritores de forma 3D
• Palavras-chave em Inglês: Breast cancer diagnosis; Mammography; Shape distribution; Dental shape descriptors; 3D shape descriptors
• Área do conhecimento CNPq: Metodologia e Técnicas da Computação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2140

Breast cancer is the second major cause of death by cancer in the female population and the fifth leading cause of death from cancer overall. However, it is known that breast cancer has a better prognosis and higher chances of cure if diagnosed at early stages. Therefore, an early detection is extremely important and the more information is available to the expert, the greater the chances of a correct diagnosis. This scenario justifies the need for the development of computational techniques to support the early detection of breast cancer. Therefore, the purpose of this work is to present a method for breast cancer classification from mammography images using shape analysis and pattern recognition techniques. For that, it is investigated the use of the descriptors Relief Index, Average Slope, Section Area, Section Convolution, D1dist, D2dist and D3dist. These shape descriptors are not traditional in the context of medical images analysis. They are so called \dental" shape descriptors, which have been used in dental ecology as ecometrics, characteristics of organisms that reflect a species’ ecology, to analyze dental shape of mammals and reconstruct past environments. Several experiments with combinations of descriptors are performed, producing several feature vectors. Then, these vectors are submitted to the support vector machine classifier. The proposed method revealed promising results. The best result, on average, was 92.58% accuracy, 92.80% sensitivity and 92.28% specificity