Diagnóstico de câncer de mama em imagens mamográficas através de características locais e invariantes

• Ano de defesa: 2017
• Autor(a) principal: MATOS, Caio Eduardo Falcão
• Orientador(a): BRAZ JÚNIOR, Geraldo
• 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 CIÊNCIA DA COMPUTAÇÃO/CCET
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: Reconhecimento de Padrões; Câncer de Mama; Mamografia; Detecção de Características; Representação de Características
• Palavras-chave em Inglês: Pattern Recognition; Breast Cancer; Mammography; Feature Detection; Feature Representation
• Área do conhecimento CNPq: Modelos Analíticos e de Simulação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1324

Breast cancer is one of the leading causes of death among women over the world. The high mortality rates that cancers achieves across the world highlight the importance of developing and investigating the means for the early detection and diagnosis of this disease. Computer Detection and Diagnosis Systems (Computer Assisted Detection / Diagnosis) have been used and proposed as a way to help health professionals. This work proposes a new methodology for discriminating patterns of malignancy and benignity of masses in mammography images by analysis of local characteristics. To do so, it is proposed a combined methodology of feature detectors and descriptors with a model of data representation for an analysis. The goal is to capture both texture and geometry in areas of mammograms. We use the SIFT, SURF and ORB detectors, and the descriptors HOG, LBP, BRIEF and Haar Wavelet. The generated characteristics are coded by a bag of features model to provide a new representation of the data and therefore decrease a dimensionality of the space of characteristics. Finally, this new representation is classified using three approaches: Support Vector Machine, Random Forest, and Adaptive Boosting to differentiate as malignant and benign masses. The methodology provides promising results for the diagnosis of malignant and benign mass encouraging that as local characteristics generated by descriptors and detectors produce a satisfactory a discriminating set.