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Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation

Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation
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摘要 In this paper, elitist reconstruction genetic algorithm (ERGA) based on Markov random field (MRF) is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms. In this paper, elitist reconstruction genetic algorithm (ERGA) based on Markov random field (MRF) is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.
出处 《Journal of Electronic Science and Technology》 CAS 2012年第1期83-87,共5页 电子科技学刊(英文版)
关键词 Elitist reconstruction genetic algorithm image segmentation Markov random field. Elitist reconstruction, genetic algorithm, image segmentation, Markov random field.
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