Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (2): 12-20.doi: 10.16180/j.cnki.issn1007-7820.2021.02.003

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A Novel Medical Image Segmentation Algorithm Based on Level Set

FANG Jinli1,LÜ Yibin1,WANG Yingzi2,TANG Shengnan1,WU Dean3   

  1. 1. Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China
    2. Computer Center,Kunming University of Science and Technology,Kunming 650500,China
    3. School of Mathematical Sciences,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2019-12-02 Online:2021-02-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(11461037)


In view of the common inhomogeneous intensity effect in medical images, a hybrid level set model combining global and local term is proposed, namely HLSGL. Based on the global information of the C-V level set segmentation algorithm, the local information energy term is introduced by calculating the local fitting average of each pixel of the image, so that the global and local information can be superimposed to form the driving force term, ensuring better localization effect on the edge of the image. A new speed stopping function is introduced into the driving force term to adaptively adjust the curve evolution rate in segmentation process, improve segmentation efficiency. In addition, HLSGL is applied to different kinds of medical images in the present study, and experiments results show that the HLSGL model can efficiently segment medical images with noise, weak boundaries and inhomogeneous intensity to obtain a more complete contour curve. Compared with other level set models,the accuracy, robustness and segmentation efficiency of the HLSGL model are significantly improved.

Key words: medical image segmentation, inhomogeneous intensity, level set, C-V model, global and local, speed stopping function

CLC Number: 

  • TP391.41