Segmentaçao de tecidos cerebrais em imagens de ressonância magnética utilizando campos aleatórios de Markov

Orientador: Klaus de GeusDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2003Inclui bibliografiaResumo: A ressonância magnética é uma modalidade de imagens que tem muitas aplicações em medicina e em especial em estudos do cérebro. Um dos seus pontos fortes é o alto contraste que produz em tecidos moles, o qual possibilita a utilização de imagens em diagnósticos de anomalias e planejamento de procedimentos cirúrgicos. Este trabalho investiga métodos de segmentação de tecidos cerebrais que usam campos aleatórios de Markov e algoritmos genéticos. O algoritmo genético tem o objetivo de melhorar o processo de segmentação por meio da determinação de parâmetros iniciais. Os resultados obtidos neste processo são comparados com imagens segmentadas manualmente por especialistas. Além disso, o resultado da segmentação permite a classificação de estruturas e a determinação de novos parâmetros, os quais auxiliam no processo de criação de imagens tridimensionais do cérebro.Abstract: Magnetic resonance is an imaging modality with many applications in medicine, particularly in brain studies. One advantage of its use is the high contrast that it generates in soft tissues, allowing for its use in the diagnosis of anomalies and in the planning of surgical procedures. The present work investigates methods of brain tissue segmentation that use Markov random fields and genetic algorithms. A genetic algorithm is employed to estimate initial parameters, aiming at improving the segmentation process. The results thus obtained are compared with images that were manually segmented by specialists. In addition, the results of the segmentation process also make it possible to classify structures and determine new parameters, which are useful in the creation of three dimension images of the brain

Optimal design and control of stationary electrochemical double-layer capacitors for light railways

The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%. The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26 - 27%