Journal of Fusion Energy

, Volume 34, Issue 6, pp 1348–1355 | Cite as

Plasma Shape Identification and Fitting on the Basis of Image Processing on Tokamak

  • Erbing Xue
  • Xianmei Zhang
  • Jiarong Luo
  • Limin Yu
Original Research


To perform experiments and data evaluation in tokamak devices, plasma shape identification is needed for plasma equilibrium control and analysis. A new framework for plasma shape identification is currently being developed in EAST. The foundations of the system are high speed CCD, the remote computer and the algorithms for plasma boundary identification and fitting. In this paper, a new quasi-real time method for plasma shape identification, based on the modified Canny algorithm and carried out in EAST, is proposed. Comparison with the result of EFIT and other diagnostic data, our method has not only been verified the stability and dependability of the detection method, but also revealed it being a reliable tool for operators and physicists to analyze the behavior of plasma. The real time plasma shape identification can be introduced to the plasma control system in the coming campaign of EAST.


Image processing Boundary detection Fitting Tracking Canny algorithm 



This work was supported by the Chinese National Natural Science Foundation Contract No 11405058, and the Chinese Ministry of Science and Technology Contract No 2013GB106020.


  1. 1.
    D.W. Swain, G.H. Neilson, Nucl. Fusion 22, 1015 (1982)CrossRefGoogle Scholar
  2. 2.
    B.J. Braams, Plasma Phys. Control. Fusion 33, 715 (1991)CrossRefADSGoogle Scholar
  3. 3.
    Y.K. Kuzenetsoy et al., Nucl. Fusion 38, 1829 (1998)CrossRefADSGoogle Scholar
  4. 4.
    L.L. Lao et al., Nucl. Fusion 25, 1611 (1985)CrossRefGoogle Scholar
  5. 5.
    S.A. Sabbagh et al., Nucl. Fusion 41, 1601 (2001)CrossRefADSGoogle Scholar
  6. 6.
    K. Kurihara, Fusion Eng. Des. 51, 1049 (2000)CrossRefGoogle Scholar
  7. 7.
    D. Guilhem et al., Fusion Eng. Des. 74, 879 (2005)CrossRefGoogle Scholar
  8. 8.
    P. Moreau et al., Fusion Eng. Des. 82, 1030 (2007)CrossRefGoogle Scholar
  9. 9.
    Li Xin et al., IEEE Trans. Image Process. 10, 311 (2000)Google Scholar
  10. 10.
    C.H. Kim et al., IEEE Trans. Circuits Syst. Video Technol. 13, 549 (2003)CrossRefGoogle Scholar
  11. 11.
    A.M. Darwish et al., IEEE Trans. Vision Image Signal Process. 144, 207 (1997)CrossRefGoogle Scholar
  12. 12.
    Yang Zhenya et al., Comput. Appl. Softw. 21, 87 (2004)Google Scholar
  13. 13.
    Sha Sha et al., J. Image Graph. 15, 490 (2010)Google Scholar
  14. 14.
    Mu Kang et al., J. Xi’an Jiaotong Univ. 42, 1240 (2008)MATHGoogle Scholar
  15. 15.
    Andrew Fitsgibbon et al., IEEE Trans. Pattern Anal. Mach. Intell. 21, 476 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Erbing Xue
    • 1
  • Xianmei Zhang
    • 1
  • Jiarong Luo
    • 2
  • Limin Yu
    • 1
  1. 1.Department of Physics, School of ScienceEast China University of Science and TechnologyXuhui, ShanghaiChina
  2. 2.Department of Physics, College of ScienceDonghua UniversityShanghaiChina

Personalised recommendations