Detecção automática de massas em imagens mamográficas usando particle swarm optimization (PSO) e índice de diversidade funcional

• Ano de defesa: 2016
• Autor(a) principal: Silva Neto, Otilio Paulo da
• Orientador(a): SILVA, Aristófanes Corrêa
• 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: Engenharia
• País: BR
• Palavras-chave em Português: Particle Swarm Optimization (PSO); Graph Clustering; Câncer de Mama; Índice de Diversidade Funcional
• Palavras-chave em Inglês: Particle Swarm Optimization(PSO); Índice de Diversidade Funcional; Graph Clustering; Câncer de Mama; Mamografia
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/298

Breast cancer is now set on the world stage as the most common among women and the second biggest killer. It is known that diagnosed early, the chance of cure is quite significant, on the other hand, almost late discovery leads to death. Mammography is the most common test that allows early detection of cancer, this procedure can show injury in the early stages also contribute to the discovery and diagnosis of breast lesions. Systems computer aided, have been shown to be very important tools in aid to specialists in diagnosing injuries. This paper proposes a computational methodology to assist in the discovery of mass in dense and nondense breasts. This paper proposes a computational methodology to assist in the discovery of mass in dense and non-dense breasts. Divided into 6 stages, this methodology begins with the acquisition of the acquired breast image Digital Database for Screening Mammography (DDSM). Then the second phase is done preprocessing to eliminate and enhance the image structures. In the third phase is executed targeting with the Particle Swarm Optimization (PSO) to find regions of interest (ROIs) candidates for mass. The fourth stage is reduction of false positives, which is divided into two parts, reduction by distance and clustering graph, both with the aim of removing unwanted ROIs. In the fifth stage are extracted texture features using the functional diversity indicia (FD). Finally, in the sixth phase, the classifier uses support vector machine (SVM) to validate the proposed methodology. The best values found for non-dense breasts, resulted in sensitivity of 96.13%, specificity of 91.17%, accuracy of 93.52%, the taxe of false positives per image 0.64 and acurva free-response receiver operating characteristic (FROC) with 0.98. The best finds for dense breasts hurt with the sensitivity of 97.52%, specificity of 92.28%, accuracy of 94.82% a false positive rate of 0.38 per image and FROC curve 0.99. The best finds with all the dense and non dense breasts Showed 95.36% sensitivity, 89.00% specificity, 92.00% accuracy, 0.75 the rate of false positives per image and 0, 98 FROC curve.