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An Adaptive Stopping Active Contour Model for Image Segmentation

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

Abstract

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.

Keywords

Image segmentation Active contour model Reaction diffusion Adaptive stopping method 

Notes

Acknowledgements

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).

References

  1. 1.
    Liu Q, Jiang M, Bai P (2016) A novel level set model with automated initialization and controlling parameters for medical image segmentation. Comput Med Imaging Graph 48:21–29CrossRefGoogle Scholar
  2. 2.
    Wu P, Liu Y, Liu B (Jun. 2015) Robust prostate segmentation using intrinsic properties of TRUS images. IEEE Trans Med Imaging 34:1321–1335CrossRefGoogle Scholar
  3. 3.
    Yang X, Gao X, Tao D (Jan. 2015) An efficient MRF embedded level set method for image segmentation. IEEE Trans Image Process 24:9–21MathSciNetCrossRefGoogle Scholar
  4. 4.
    Goldenberg R, Kimmel R, Rivlin E (Oct. 2001) Fast geodesic active contours. IEEE Trans Image Process 10:1467–1475MathSciNetCrossRefGoogle Scholar
  5. 5.
    Li C, Liu J, Fox MD (2005) Segmentation of external force field for automatic initialization and splitting of snakes. Pattern Recognit 38:1947–1960CrossRefGoogle Scholar
  6. 6.
    Li C, Xu C, Gui C (2005) Level set evolution without re-initialization: a new variational formulation. In: Proceedings of the IEEE (CVPR 2005), San Diego, USA, vol. 1, pp. 430–436Google Scholar
  7. 7.
    Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Chan TF, Vese LA (Feb. 2001) Active contours without edges. IEEE Trans Image Process 10:266–277CrossRefzbMATHGoogle Scholar
  9. 9.
    Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the mumford and shah model. Int J Comput Vis 50:271–293CrossRefzbMATHGoogle Scholar
  10. 10.
    Li C, Kao CY, Gore JC (2007) Implicit active contours driven by local binary fitting energy. In: IEEE conference (CVPR2007), Minneapolis, USA, pp. 1–7Google Scholar
  11. 11.
    Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72:195–215CrossRefGoogle Scholar
  12. 12.
    Zhou S, Wang J, Zhang S (2016) Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing 186:107–118CrossRefGoogle Scholar
  13. 13.
    Li C, Kao CY, Gore JC (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17:1940–1949MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wang L, He L, Mishra A (Mar. 2009) Active contours driven by local Gaussian distribution fitting energy. Signal Process 89:2435–2447CrossRefzbMATHGoogle Scholar
  15. 15.
    Zhang K, Song H, Zhang L (Oct. 2010) Active contours driven by local image fitting energy. Pattern Recognit 43:1199–1206CrossRefzbMATHGoogle Scholar
  16. 16.
    Zhang K, Zhang L, Zhang S (2010) A variational multiphase level set approach to simultaneous segmentation and bias correction. In: IEEE international conference (ICIP 2007), pp. 4105–4108Google Scholar
  17. 17.
    Wang L, Li C, Sun Q (Apr. 2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 33:520–531CrossRefGoogle Scholar
  18. 18.
    Wang H, Huang TZ, Xu Z (Nov. 2014) An active contour model and its algorithms with local and global Gaussian distribution fitting energies. Inf Sci 263:43–59CrossRefGoogle Scholar
  19. 19.
    Jiang X, Wu X, Xiong Y (2015) Active contours driven by local and global intensity fitting energies based on local entropy. Optik 126:5672–5677CrossRefGoogle Scholar
  20. 20.
    Shi N, Pan J (2016) An improved active contours model for image segmentation by level set method. Optik 127:1037–1042CrossRefGoogle Scholar
  21. 21.
    Yuan S, Monkam P, Li S, Song H, Zhang F (2017) Active contour model via local and global intensity information for image segmentation. In: Proceedings of the 36th Chinese control conference, Dalian, China, pp. 5618–5623Google Scholar
  22. 22.
    Li C, Xu C, Gui C (Dec. 2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19:3243–3254MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zhang K, Zhang L, Song H (Jan. 2013) Reinitialization-free level set evolution via reaction diffusion. IEEE Trans Image Process 22:258–271MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Wang XF, Huang DS, Xu H (2010) An efficient local Chan-Vese model for image segmentation. Pattern Recognit 43:603–618CrossRefzbMATHGoogle Scholar
  25. 25.
    Zhang K, Zhang L, Lam KM, Zhang D (Feb. 2016) A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans Cybern 46:546–557CrossRefGoogle Scholar
  26. 26.
    Zhao L, Zheng S, Wei H, Gui L (2017) Adaptive active contour model driven by global and local intensity fitting energy for image segmentation. Optik 140:908–920CrossRefGoogle Scholar
  27. 27.
    Dai L, Ding J, Yang J (Mar. 2015) Inhomogeneity-embedded active contour for natural image segmentation. Pattern Recognit 48:2513–2529CrossRefGoogle Scholar
  28. 28.
    Xie X, Wang C, Zhang A, Meng X (2014) A robust level set method based on local statistical information for noisy image segmentation. Optik 125:2199–2204CrossRefGoogle Scholar
  29. 29.
    Everingham M, Gool L, Williams I (2010) The PASCAL visual object classes challenge. Int J Comput Vis 88:303–338CrossRefGoogle Scholar

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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