Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (1): 6-10.DOI: 10.12068/j.issn.1005-3026.2019.01.002

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Fast Image Segmentation Algorithm Based on Parametric Level Set Active Contour Model

CHEN Hong1,2, YU Xiao-sheng1, WU Cheng-dong1, SUN Peng2   

  1. 1. School of Robot Science and Engineering, Northeastern University, Shenyang 110819, China; 2. College of Physics Science and Technology, Anshan Normal University, Anshan 114005, China.
  • Received:2017-10-27 Revised:2017-10-27 Online:2019-01-15 Published:2019-01-28
  • Contact: CHEN Hong
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Abstract: In order to improve the segmentation speed, a fast image segmentation method based on parametric level set active contour model was proposed. The level set function was determined by the parameter vector, rather than the signed distance function, which reduces the dimension of the level set function. The parametric level set function was embedded into the classical LGDF(local Gaussian distribution fitting) segmentation algorithm, and it does not need to be re-initialized or additional regular terms, and it can choose larger step length. The experiment results show that the proposed method can effectively segment medical images such as ultrasound, CT and MR medical images. Compared with the LGDF model with regular terms and the recently proposed fast segmentation algorithm MSLCV, in the case of similar segmentation accuracy, the calculation speed of the proposed method is improved obviously.

Key words: level set, active contour model, image segmentation, LGDF model, MSLCV model

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