吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (01): 161-165.

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Self-adapting segmentation for brain tissue

JIA Di1,2, YANG Jin-zhu1, ZHANG Yi-fei2, ZHAO Da-zhe1, YU Ge1   

  1. 1. Key Laboratory of Medical Image Computing of Ministry of Education, Northeast University,Shenyang 110179,China;
    2. School of Electronics and Information Engineering,Liaoning Technical University,Huludao 125105,China
  • Received:2010-09-12 Online:2012-01-01 Published:2012-01-01

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Abstract:

A method for 2D and 3D brain tissue segmentation was presented. First, the noise of spinal fluid affecting the segmentation accuracy was eliminated using an improved C-V model. Then, the ventricle extraction and skull stripping were performed by C-V model and regions merging with tags. Finally, the white matter and grey matter were extracted through covered background method and the brain tissue was segmented. Simulation data was used for theoretical analysis, and the results were verified by real data. The accuracy, universality and practicality were validated by experiment results.

Key words: computer application, level set, C-V model, regions merging, brain tissue segmentation

CLC Number: 

  • TP391


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