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A Fast Algorithm for Image Segmentation Based on Global Cosine Fitting Energy Model

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Parallel Architectures, Algorithms and Programming (PAAP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1362))

Abstract

Image segmentation plays an important role in computer vision and image processing. Level set model is a classical image segmentation method. During level set evolution, almost all the level set energy minimization of image segmentation are based on the gradient descent method and the finite difference scheme. The speed of evolution is slow and easy to fall into local minima. In this paper, we propose a fast sweeping optimization algorithm to minimize global cosine fitting (GCF) model. When moving a pixel from the one side region to the another side region of evolving contour, the sweeping algorithm calculates the energy change directly and checks whether the cosine fitting energy is decreased. With this, we can avoid solving the Euler-Lagrange equation and the partial differential equation, which usually take a lot of time. Moreover, our proposal is robust to initial level set contour and it automatically handles the topological variety, algorithm automatic termination and no longer requires the reinitialization step, parameter adjustment and the distance regularization term. The experiments on real noise and synthetic images show the effectiveness of the sweeping algorithm.

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References

  1. Gong, X.-Y., Su, H., Xu, De., Zhang, Z.-T., Shen, F., Yang, H.-B.: An overview of contour detection approaches. Int. J. Autom. Comput. 15(6), 656–672 (2018). https://doi.org/10.1007/s11633-018-1117-z

    Article  Google Scholar 

  2. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)

    Article  Google Scholar 

  3. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  Google Scholar 

  4. Wang, X.F., Zou, L., Xu, L.X., Lv, G., Tang, C.: Hybrid level set method based on image diffusion. Neurocomputing 228, 53–64 (2017)

    Article  Google Scholar 

  5. Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)

    Article  MathSciNet  Google Scholar 

  6. Li, C.M., Kao, C.Y., Gore, J.C., Ding, Z.H.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  7. Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)

    Article  Google Scholar 

  8. Zhang, K.H., Song, H.H., Zhang, L.: Active contours driven by local image fitting energy. Pattern Recogn. 43(4), 1199–1206 (2010)

    Article  Google Scholar 

  9. Li, C.M., Huang, R., Ding, Z.H., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)

    Article  MathSciNet  Google Scholar 

  10. Min, H., Lu, J., Jia, W., Zhao, Y., Luo, Y.: An effective local regional model based on salient fitting for image segmentation. Neurocomputing 311, 245–259 (2018)

    Article  Google Scholar 

  11. Wang, Y., Huang, T.Z., Wang, H.: Region-based active contours with cosine fitting energy for image segmentation. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 32(11), 2237–2246 (2015)

    Article  Google Scholar 

  12. Adalsteinsson, D., Sethian, J.A.: A fast level set method for propagating interfaces. J. Comput. Phys. 118, 269–277 (1995)

    Article  MathSciNet  Google Scholar 

  13. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. U.S.A. 93, 1591–1595 (1996)

    Article  MathSciNet  Google Scholar 

  14. Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Fast geodesic active contours. IEEE Trans. Image Process. 10, 1467–1475 (2001)

    Article  MathSciNet  Google Scholar 

  15. Yang, Y., Liu, X.: A robust semi-supervised learning approach via mixture of label information. Pattern Recogn. Lett. 68, 15–21 (2015)

    Article  Google Scholar 

  16. Wang, X.F., Min, H., Zou, L., Zhang, Y.G., Tang, Y.Y., Chen, C.L.P.: An efficient level set method based on multi-scale image segmentation and hermite differential operator. Neurocomputing 188, 90–101 (2016)

    Article  Google Scholar 

  17. Song, B., Chan, T.: A Fast Algorithm for Level Set Based Optimization, CAM-UCLA 68 , pp. 02–68 (2002)

    Google Scholar 

  18. He, L., Osher, S.: Solving the Chan-Vese model by a multiphase level set algorithm based on the topological derivative. In: International Conference on Scale Space and Variational Methods in Computer Vision, pp. 777–788 (2007)

    Google Scholar 

  19. Zou, J.Y., Ma, Y.C.: Pre-sweeping algorithm for solving the Chan-Vese model in image segmentation. Chin. J. Eng. Math. 27(6), 1096–1104 (2010)

    MathSciNet  MATH  Google Scholar 

  20. Krinidis, S., Chatzis, V.: Fuzzy energy-based active contours. IEEE Trans. Image Process. 18(12), 2747–2755 (2009)

    Article  MathSciNet  Google Scholar 

  21. Shyu, K.K., Pham, V.T., Tran, T.T., et al.: Global and local fuzzy energy-based active contours for image segmentation. Nonlinear Dyn. 67(2), 1559–1578 (2011)

    Article  MathSciNet  Google Scholar 

  22. Shyu, K.K., Tran, T.T., Pham, V.T., et al.: Fuzzy distribution fitting energy-based active contours for image segmentation. Nonlinear Dyn. 69(1–2), 295–312 (2012)

    Article  MathSciNet  Google Scholar 

  23. Boutiche, Y.: Fast algorithm to minimize model combining dynamically local and global fitting energy for image segmentation. In: International Conference on Control, Engineering & Information Technology, pp. 1–6. IEEE (2015)

    Google Scholar 

  24. Boutiche, Y., Abdesselam, A.: Fast algorithm for hybrid region-based active contours optimisation. IET Image Proc. 11(3), 200–209 (2017)

    Article  Google Scholar 

  25. Zou, L., et al.: A fast algorithm for image segmentation based on local Chan Vese model. In: In: Huang, D.S., Jo, K.H., Zhang, X.L. (eds.) Intelligent Computing Theories and Application. International Conference on Intelligent Computing, pp. 54–60. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95933-7_7

  26. Zou, L., et al.: Image segmentation based on Local Chan Vese model by employing cosine fitting energy. In: Lai, J.-H., et al. (eds.) PRCV 2018. LNCS, vol. 11256, pp. 466–478. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03398-9_40

    Chapter  Google Scholar 

  27. Boutiche, Y., Abdessalem, A., Ramou, N., Chetih, N.: Fast generalized Chan-Vese model for plant/soil segmentation to estimate percentage of ground cover in agricultural images. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1–5 (2019)

    Google Scholar 

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Acknowledgements

The authors would like to express their thanks to the referees for their valuable suggestions. This work was supported by the grant from the National Natural Science Foundation of China, Nos. 61672204 and 61806068, the Natural Science Foundation of Anhui Provincial, No. 1908085MF184, 1908085QF285, in part by the Key Research Plan of Anhui Province, No. 201904d07020002.

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Authors and Affiliations

  1. School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, Anhui, China

    Yanping Chen, Le Zou & Xiaofeng Wang

  2. Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, Anhui, China

    Zhize Wu

  3. College of Bioengineering Food and Environmental Sciences, Hefei University, Hefei, 23060, Anhui, China

    Qianjing Huang

Authors
  1. Yanping Chen
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  2. Le Zou
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  3. Zhize Wu
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  4. Qianjing Huang
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  5. Xiaofeng Wang
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Corresponding author

Correspondence to Xiaofeng Wang .

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Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Li Ning

  2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Vincent Chau

  3. The University of Hong Kong, Hong Kong, China

    Francis Lau

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Chen, Y., Zou, L., Wu, Z., Huang, Q., Wang, X. (2021). A Fast Algorithm for Image Segmentation Based on Global Cosine Fitting Energy Model. In: Ning, L., Chau, V., Lau, F. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, vol 1362. Springer, Singapore. https://doi.org/10.1007/978-981-16-0010-4_17

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  • DOI: https://doi.org/10.1007/978-981-16-0010-4_17

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  • Print ISBN: 978-981-16-0009-8

  • Online ISBN: 978-981-16-0010-4

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