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Clustering mechanism for electric tomography imaging

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Abstract

Electrical tomography (ET) imaging, developed in the 1980s, has attracted much industrial and research attentions owing to its low cost, quick response, lack of radiation exposure, and non-intrusiveness compared to other tomography modalities. However, to date applications thereof have been limited owing to its low imaging resolution. The issue with space resolution in existing ET imaging reconstruction methods is that they employ a mathematical approach based on an ill-posed equation with inconsistent solutions. In this paper, we propose a novel ET imaging method based on a data-driven approach. By recovering the cluster structures hidden in the ET imaging process followed by the application of a fuzzy clustering algorithm to identify the cluster structures, there is no need to study the ill-posed mathematical formulation. The proposed method has been tested by means of three experiments, including image reconstructions of a human lung image and plastic rode shape, as well as two simulations executed on the Comsol platform. The results show that the proposed method can reconstruct ET images with much higher space resolution more quickly than the existing algorithms.

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References

  1. Inez F. Electrical impedance tomography (EIT) in applications related to lung and ventilation: a review of experimental and clinical activities. Physiol Meas, 2000, 21: 1–12

    Article  Google Scholar 

  2. Trevor Y. Status of electrical tomography in industrial applications. J Electron Imaging, 1990, 10: 608–619

    Google Scholar 

  3. Clay M, Ferree T. Weighted regularization in electrical impedance tomography with applications to acute cerebral stroke. IEEE Trans Med Imaging, 2002, 21: 629–637

    Article  Google Scholar 

  4. William R. EIT reconstruction algorithms: pitfalls, challenges and recent developments. Physiol Meas, 2004, 25: 125–142

    Article  Google Scholar 

  5. Marashdeh Q, Fan L, Du B, et al. Electrical capacitance tomography-a perspective. Ind Eng Chem Res, 2008, 47: 3708–3719

    Article  Google Scholar 

  6. Du B, Warsito W, Fan L. Imaging the choking transition in gas-solid risers using electrical capacitance tomography. Ind Eng Chem Res, 2006, 45: 5384–5395

    Article  Google Scholar 

  7. Yin W, Peyton A J. A planar EMT system for the detection of faults on thin metallic plates. Meas Sci Technol, 2006, 17: 2130–2135

    Article  Google Scholar 

  8. Hayt W H, Buck J A. Engineering Electromagnetic, 7th ed. New York: McGraw-Hill, 2006

    Google Scholar 

  9. Yang W, Liu S. Electrical capacitance tomography with square sensor. Electron Lett, 1999, 35: 295–296

    Article  Google Scholar 

  10. Cao Z, Wang H, Xu L. Electrical impedance tomography with an optimized calculable square sensor. Rev Sci Instrum, 2008, 79: 103710–103719

    Article  Google Scholar 

  11. Santosa F, Vogelius M. A back projection algorithm for electrical impedance imaging. SIAM J Appl Math, 1990, 50: 216–243

    Article  MathSciNet  MATH  Google Scholar 

  12. Cheney M, Isaacson D, Newell J C, et al. Noser: an algorithm for solving the inverse conductivity problem. Int J Imaging Syst Technol, 1990, 6: 266–275

    Google Scholar 

  13. Vauhkonen M, Vadasz D, Karjalainen P A, et al. Tikhonov regularization and prior information in electrical impedance tomography. IEEE Trans Med Imaging, 1998, 17: 285–293

    Article  Google Scholar 

  14. Hu L, Wang H X, Zhao B et al. A hybrid reconstruction algorithm for electrical impedance tomography. Meas Sci Technol, 2007, 18: 813–818

    Article  Google Scholar 

  15. Yang W Q, Spink D M, York T A, et al. An image-reconstruction algorithm based on Landweber’s iteration method for electrical-capacitance tomography. Meas Sci Technol, 1999, 10: 1065–1069

    Article  Google Scholar 

  16. Player M A, van Weereld J, Allen A R, et al. Truncated-Newton algorithm for three dimensional electrical impedance tomography. Electron Lett, 1999, 35: 2189–2191

    Article  Google Scholar 

  17. Bikowski J, Mueller J. 2D EIT reconstructions using Calderon’s method. Inverse Probl Imaging, 2008, 2: 43–61

    Article  MathSciNet  MATH  Google Scholar 

  18. Vauhkonen M. Electrical impedance tomography and prior information. Dissertation for the Doctoral Degree. University of Kuopio, 1997

  19. Wang M. Inverse solutions for electrical impedance tomography based on conjugate gradients methods. Meas Sci Technol, 2002, 13: 101–117

    Article  Google Scholar 

  20. Polydorides N. Image reconstruction algorithm for soft-field tomography. Dissertation for the Doctoral Degree. University of Manchester Institute of Science and Technology, 2002

  21. Vogel C R. Computational Methods for Inverse Problems. Philadelphia: SIAM, 2002

    Book  MATH  Google Scholar 

  22. Xu R, Wunsch D. Survey of clustering algorithm. IEEE Trans Neural Netw, 2005, 16: 645–678

    Article  Google Scholar 

  23. Yue S, Wang J, Wu T. A new separation measure to improve the effectiveness of the clustering validation evaluation. Inf Sci, 2010, 80: 748–764

    Article  MathSciNet  Google Scholar 

  24. Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981

    Book  MATH  Google Scholar 

  25. Yue S, Wang J, Wu T. A new unsupervised approach to clustering. Sci China Inf Sci, 2010, 189: 1345–1357

    Article  Google Scholar 

  26. Wu K, Yang M. Alternative c-means clustering algorithms. Pattern Recognit, 2002, 35: 2267–2278

    Article  MATH  Google Scholar 

  27. Xie X L, Beni G. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell, 1991, 13: 841–847

    Article  Google Scholar 

  28. Taflove A, Hagness S C. Computational Electromagnetic: The Finite Difference Time-Domain Method, 3rd ed. Boston: Artech House, 2005

    Google Scholar 

  29. Ni G Z, Yang S Y, Qian X Y, et al. Numerical Calculation of Engineering Electromagnetic Field (in Chinese). Beijing: Machinery Industry Press, 2004

    Google Scholar 

  30. Murai T, Kagawa Y. Electrical impedance computed tomography based on a finite element model. IEEE Trans Biomed Eng, 1985, 32: 177–184

    Article  Google Scholar 

  31. Wu K, Yang M. Alternative c-means clustering algorithms. Pattern Recognit, 2002, 35: 2267–2278

    Article  MATH  Google Scholar 

  32. Zhang D, Chen S. Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Process Lett, 2003, 18: 155–162

    Article  Google Scholar 

  33. Yang W Q, Spink D M, York T A, et al. An image-reconstruction algorithm based on Landweber’s iteration method for electrical-capacitance tomography. Meas Sci Technol, 1999, 10: 1065–1069

    Article  Google Scholar 

  34. Soleimani M, Lionheart W. Nonlinear image reconstruction for electrical capacitance tomography using experimental data. Meas Sci Technol, 2005, 16: 1987–1996

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China

    ShiHong Yue, LiJun Cui & HuaXiang Wang

  2. School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, 85287-0112, USA

    Teresa Wu

Authors
  1. ShiHong Yue
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  2. Teresa Wu
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  3. LiJun Cui
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  4. HuaXiang Wang
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Correspondence to ShiHong Yue.

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Yue, S., Wu, T., Cui, L. et al. Clustering mechanism for electric tomography imaging. Sci. China Inf. Sci. 55, 2849–2864 (2012). https://doi.org/10.1007/s11432-012-4748-7

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  • DOI: https://doi.org/10.1007/s11432-012-4748-7

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