SEGMENTAÇÃO AUTOMÁTICA DE NÓDULOS PULMONARES COM GROWING NEURAL GAS E MÁQUINA DE VETORES DE SUPORTE

• Ano de defesa: 2010
• Autor(a) principal: Netto, Stelmo Magalhães Barros
• 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: Detecção auxiliada por computador (CAD); Nódulo pulmonar; Growing Neural Gas (GNG); Tomografia computadorizada
• Palavras-chave em Inglês: Computer-aided detection (CAD); Lung nodule; Growing Neural Gas (GNG); Computerized tomography
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::MATEMATICA DA COMPUTACAO::MODELOS ANALITICOS E DE SIMULACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/431

Lung cancer is still one of the most frequent types throughout the world. Its diagnosis is very difficult because its initial morphological characteristics are not well defined, and also because of its location in relation to the lung. It is usually detected late, fact that causes a large lethality rate. Facing these difficulties, many researches are done, concerning both detection and diagnosis. The objective of this work is to propose a methodology for computer-aided automatic lung nodule detection. The return of the development of such methodology is that its application will aid the doctor in the simultaneous detection of several nodules present in computerized tomography images. The methodology developed for automatic detection of lung nodules involves the use of a method of competitive learning, called Growing Neural Gas (GNG). The methodology still consists in the reduction of the volume of interest, by the use of techniques largely used in thorax extraction, lung extraction and reconstruction. The next stage is the application of the GNG in the resulting volume of interest, that together with the separation of the nodules from the various structures present in the lung form the segmentation stage, and, finally, through texture and geometry measurements, the classification as either nodule or non-nodule is performed. The methodology guarantees that nodules of reasonable size are found with sensibility of 86%, specificity of 91%, what results in accuracy of 91%, in average, for ten training and test experiments, in a sample of 48 nodules occurring in 29 exams. The false-positive per exam rate was of 0.138, for the 29 analyzed exams.