DETECÇÃO DE MASSAS EM IMAGENS MAMOGRÁFICAS USANDO ÍNDICE DE DIVERSIDADE DE SIMPSON E MÁQUINA DE VETORES DE SUPORTE.

• Ano de defesa: 2009
• Autor(a) principal: NUNES, André Pereira
• 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: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Mamografia; Detecção Auxiliada por Computador; K-Means; Template Matching; Índice de Diversidade de Simpson; Máquina de Vetores de Suporte
• Palavras-chave em Inglês: Mammography; Computer-Aided Detection; K-Means; Template Matching; Simpson’s Diversity Index; Support Vector Machine
• Área do conhecimento CNPq: Modelos Analíticos e de Simulação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1845

Breast cancer is one of the major causes of mortality among women throughout the world. Presently, the analysis of breast radiography is the most used method to early detection of this kind of cancer. It enables the identification of anomalies at their initial stage, which is a fundamental factor for success in the treatment. The sensitivity of this kind of exam, although, depends on several factors, such as the size and the location of the abnormalities, density of the breast tissue, quality of the technical resources and radiologist's ability. This work presents a methodology that uses the K-Means clustering algorithm and the Template Matching technique for segmentation of suspicious regions. Next, geometry and texture features are extracted from each of these regions, being the texture described by the Simpson's Diversity Index, a statistic used in Ecology to measure the biodiversity of an ecosystem. Finally, this information is submitted to a Support Vector Machine so that the suspicious regions are classified into masses and non-masses. The methodology was tested with 650 mammographic images from the DDSM database, achieving 83.94% of accuracy, 83.24% of sensibility and 84.14% of specificity in average.