DETECÇÃO DE MASSAS EM IMAGENS MAMOGRÁFICAS ATRAVÉS DO ALGORITMO GROWING NEURAL GAS E DA FUNÇÃO K DE RIPLEY

• Ano de defesa: 2007
• Autor(a) principal: Martins, Leonardo de Oliveira
• 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: Textura; Função K de Ripley; Growing Neural Gas; Detecção auxiliada por computador; Mamografia
• Palavras-chave em Inglês: Texture; Ripley’; s K Function; Growing Neural Gas; Computeraided detection; Mamography
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/510

Breast cancer is a serious public health problem in several countries of the world. Computer-Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success in aid to health care professionals. The goal of such systems is not to replace the professional, but join forces in order to early detect the different types of cancer. The main contribution of this work is to present a methodology for detecting masses in digitized mammograms using the algorithm Growing Neural Gas for the segmentation of the image and Ripley’s K function to describe the texture of segmented objects. The classification of these objects is accomplished through a Support Vector Machine (SVM), which separates them into two groups: masses and non-masses. The methodology obtained 89,30% of accuracy and a rate of 0,93 false-positive per image.