DETECÇÃO DE MASSAS EM IMAGENS MAMOGRÁFICAS USANDO REDES NEURAIS CELULARES, FUNÇÕES GEOESTATÍSTICAS E MÁQUINAS DE VETORES DE SUPORTE

• Ano de defesa: 2009
• Autor(a) principal: Sampaio, Wener Borges de
• 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: Mamografia; Detecção Auxiliada por Computador; Redes Celulares Neurais; Índice de Moran; Coeficiente de Geary; Função K de Ripley; Máquina de Vetores de Suporte
• Palavras-chave em Inglês: Mammography; Computer-Aided Detection; Cellular Neural Networks; Moran s Index; Geary s Index; Ripley s K Function; Support Vector Machine
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/417

Breast cancer presents high occurrence frequency among the world population and its psychological effects alter the perception of the patient s sexuality and the own personal image. Mammography is an x-ray of the mamma that allows the precocious detection of cancer, since it is capable to showing lesions in their initial stages, typically very small lesions in the order of millimeters. The processing of mammographic images has been contributing to the detection and the diagnosis of mammary nodules, being an important tool, because it reduces the degree of uncertainty of the diagnosis, providing a supplementary source of information to the specialist. This work presents a computational methodology that aids the specialist in the detection of breast masses. The first step of the methodology aims at improvement the mammographic image, which consists of removal of unwanted objects, reduction of noise and enhancement of the breast internal structures. Then, Cellular Neural Networks are used to segment areas suspected of containing masses. These regions have their shapes analyzed by geometry descriptors (eccentricity, circularity, compactness, circular disproportion and circular density) and their textures are analyzed using geostatistical functions (Ripley's K function, Moran's and Geary's indices). Support Vector Machine were trained and used to classify the candidate regions in one of the classes, masses or no-mass, with sensibility of 80.00%, specificity of 85.68%, acuracy of 84.62%, a rate of 0.84 false positive for image and 0.20 false negative for image and an area under the curve ROC of 0.827.