An improved method of MRI segmentation based on variational level set
DOI: 10.23977/jipta.2016.11001 | Downloads: 80 | Views: 3516
Lihong Li 1, Zuojun Liu 2
1 Faculty of Foreign Language, Huaiyin Institute of Technology, Huai’an, Jiangsu, China
2 Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, China
Corresponding AuthorZuojun Liu
Intensity inhomogeneity is a thorny problem in MRI segmentation. In order to solve this problem, an improved method of MRI segmentation is proposed in this paper. The polarity information is introduced to solve the problem and an energy penalty term is introduced to make sure that the level set function keep approaching the symbol distance function. In the method of this paper, the problem of image segmentation is attributed to a problem of minimum of the energy function with local polar information. The improved symbol distance function is built firstly. Then, the final segmentation result is got through solving the minimum value of the energy function by variational level set. Proved by lots of experiments, this method is very efficient.
KEYWORDSIntensity inhomogeneity; MRI segmentation; polar information; energy penalty term
CITE THIS PAPER
Zuojun, L. and Lihong, L. (2016) An improved method of MRI segmentation based on variational level set. Journal of Image Processing Theory and Applications (2016) 1: 1-5.
 Xu Chenyang, Prince J L. Snakes, shapes, and gradient vector flow [J]. Image Processing, IEEE Transactions on, 1998, 7(3): 359-369.
 Ronfard R. Region-based strategies for active contour models [J]. International Journal of Computer Vision, 1994, 13(2): 229-251.
 Liu Songtao, Yin Fuliang. Image segmentation method based on graph cut and the new progress [J]. Acta automatica, 2012, 38(6): 911-922.
 Osher S, Sethian J A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations [J]. Journal of Computational Physics, 1988, 79(1): 12-49.
 Cai Li, Gao Hao, Xie Wenxian. Variational level set method for left ventricle segmentation [C]//TENCON 2013-2013 IEEE Region 10 Conference (31194). Xi¢an, China, 2013: 1-4.
 Zhan Tianming, Zhang Jun, Xiao Liang, et al. An improved variational level set method for MR image segmentation and bias field correction [J]. Magnetic Resonance Imaging, 2013, 31(3): 439-447.
 Li Chunming, Xu Chenyang, Gui Changfeng, et al. Level set evolution without re-initialization: a new variational formulation [C]//Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society Conference on. Storrs, USA, 2005: 430-436.