DETECÇÃO DE NÓDULOS PULMONARES PEQUENOS USANDO MODELO DE MISTURA GAUSSIANA E MATRIZ HESSIANA

• Ano de defesa: 2011
• Autor(a) principal: Santos, Alex Martins
• 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: Processamento de Imagens; Tomografia Computadorizada; Detecção de Nódulos Pulmonares Pequenos
• Palavras-chave em Inglês: Image Processing; Computer Tomography; Small Lung Nodule Detection
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/473

Lung cancer stands out by pointing the highest incidence and higher mortality rate of all other types of cancer. It has one of the lower survival rates of after diagnosis, which is mainly due to late detection and therefore delayed treatment. Computer-aided detection systems (CAD) are developed to assist the specialist in the search and identification of nodules and changes in CT. These systems respectively aim to automate the identification and classification of these structures. This work aims to study and develop a methodology for automatic detection of small lung nodules (bigger than 2 mm and smaller than 10 mm in diameter). The proposed methodology is based on techniques of image processing and pattern recognition. Similar applications use widely some of these techniques. The proposed methodology also uses other techniques from different areas and applications, such as measures of the Tsallis and Shannon entropy used in this study to describe suspected structures. These measures are respectively provided from statistical mechanics and information theory, however lately they have been successfully applied in image processing. It was also used the Gaussian mixture model (GMM) and the Hessian matrix calculation to separate the internal structures of the remaining lung parenchyma. Promising results were found in tests with 140 exams divided in of 80% for training and 20% for testing. It was achieved a 79% of sensitivity rate and a total of 1.17 false positives per slice.