Classificação de tecidos da mama em massa e não-massa usando índice de diversidade taxonômico e máquina de vetores de suporte

• Ano de defesa: 2013
• Autor(a) principal: OLIVEIRA, Fernando Soares Sérvulo de
• Orientador(a): PAIVA, Anselmo Cardoso de
• 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; Árvores Filogenéticas; Índice de Diversidade Taxonômica; Índice de Distinção Taxonômica; Máquina de Vetores de Suporte
• Palavras-chave em Inglês: Mammography; Phylogenetic tree; Taxonomic Diversity Index; Taxonomic Distinctness Index; Support Vector Machine
• Área do conhecimento CNPq: Processamento Gráfico; Cancerologia
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1837

Breast cancer is the second most common type of cancer in the world and difficult to diagnose. Distinguished Systems Aided Detection and Diagnosis Computer have been used to assist experts in the health field with an indication of suspicious areas of difficult perception to the human eye, thus aiding in the detection and diagnosis of cancer. This dissertation proposes a methodology for discrimination and classification of regions extracted from the breast mass and non-mass. The Digital Database for Screening Mammography (DDSM) is used in this work for the acquisition of mammograms, which are extracted from the regions of mass and non-mass. The Taxonomic Diversity Index (∆) and the Taxonomic Distinctness (∆*) are used to describe the texture of the regions of interest, originally applied in ecology. The calculation of those indices is based on phylogenetic trees, which applied in this work to describe patterns in regions of the images of the breast with two regions bounding approaches to texture analysis: circle with rings and internal with external masks. Suggested in this work to be applied in the description of patterns of regions in breast imaging approaches circle with rings and masks as internal and external boundaries regions for texture analysis. Support Vector Machine (SVM) is used to classify the regions in mass or non-mass. The proposed methodology provides successful results for the classification of masses and non-mass, reaching an average accuracy of 99.67%.