Author(s)

Xianqi Cao¹, Jiaqing Miao^2,3, Yu Xiao³

¹College of Applied Technology, University of Science and Technology Liaoning, Anshan, China.
²School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China.
³Engineering and Technical College, Chengdu University of Technology, Leshan, China.

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.

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

Cao, X. , Miao, J. and Xiao, Y. (2017) Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning. World Journal of Engineering and Technology, 5, 90-96. doi: 10.4236/wjet.2017.51008.