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Zwickl, D.J. (2006) Genetic Algorithm Approaches for the Phylogenetic Analysis of Large Biological Sequence Datasets under the Maximum Likelihood Criterion. The University of Arizona, Tucson.

has been cited by the following article:

  • TITLE: Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

    AUTHORS: Xianqi Cao, Jiaqing Miao, Yu Xiao

    KEYWORDS: Dictionary, K-SVD, Matching Pursuit, Sparse Representation, Genetic Algorithm, Dual Population

    JOURNAL NAME: World Journal of Engineering and Technology, Vol.5 No.1, February 24, 2017

    ABSTRACT: The image signal is represented by using the atomic of image signal to train an over complete dictionary and is described as sparse linear combinations of these atoms. Recently, the dictionary algorithm for image signal tracking and decomposition is mainly adopted as the focus of research. An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition. A new segmentation algorithm of brain MRI image, which uses the noise reduction method with adaptive dictionary based on genetic algorithm, is presented in this paper, and the experimental results show that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation. In a very complicated situation, the results show that the segmentation of brain MRI images can be accomplished successfully by using this algorithm, and it achieves the ideal effect and has good accuracy.