Journal of Electrical Engineering & Technology

, Volume 14, Issue 1, pp 445–453 | Cite as

An Adaptive Stopping Active Contour Model for Image Segmentation

  • Yuefeng NiuEmail author
  • Jianzhong Cao
  • Zuofeng Zhou
Original Article


Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.


Image segmentation Active contour model Reaction diffusion Adaptive stopping method 



This research is supported by the Youth Science and Technology New Star of Shaanxi Province (no. 2016KJXX-01), and partially supported by the Western Light of the Chinese Academy of Science (no. Y429611213).


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Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of SciencesXi’anChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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