Técnicas de computação natural para segmentação de imagens médicas

• Main Author: Souza, Jackson Gomes de
• Publication Date: 2009
• Format: Master thesis
• Language: por
• Source: Repositório Institucional da UFRN
• Download full: https://repositorio.ufrn.br/jspui/handle/123456789/15282
• topic: Processamento de imagens digitais; segmentação de imagens médicas; otimização por enxame de partículas; computação natural; algoritmos genéticos; k-means; fuzzy c-means; Digital image processing; medical image segmentation; particle swarm optimization; natural computing; genetic algorithms; k-means; fuzzy c-means; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth