Clustering-Based Particle Swarm Optimization for Electrical Impedance Imaging

  • Gang Hu
  • Min-you Chen
  • Wei He
  • Jin-qian Zhai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6728)


An attempt has been made in this paper to solve the non-linear and ill-posed Electrical Impedance Tomography (EIT) inverse problem using clustering-based particle swam optimization (PSO). To enhance optimal search capability in such an ultra high dimension space and improve the quality of the reconstructed image, an adaptive PSO algorithm combined with a modified Newton–Raphson algorithm and a conductivity-based clustering algorithm was proposed. The modifications are performed on the reduction of dimension by dividing all mesh into clusters and initializing particles using the result of the modified Newton–Raphson type algorithm. Numerical simulation results indicated that the proposed method has a faster convergence to optimal solution and higher spatial resolution on a reconstructed image.


Particle Swarm Optimization Clustering Reconstruction Algorithm Electrical Impedance Tomography 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, B.H.: Electrical impedance tomography. Journal of Medical Engineering & Technology 27(3), 97–108 (2003)CrossRefGoogle Scholar
  2. 2.
    Lionheart, W.R.B.: EIT reconstruction algorithms: pitfalls, challenges and recent developments. Physiol. Meas. 25, 125–142 (2004)CrossRefGoogle Scholar
  3. 3.
    Kimt, H.C., Mood, D.C., Kimtt, M.C., Kimttt, S., Leettt, Y.J.: Improvement in EIT Image Reconstruction using Genetic Algorithm. In: Proceedings of the American Control Conference, Anchorage, pp. 3858–3863 (2002)Google Scholar
  4. 4.
    Olmi, R., Bini, M., Priori, S.: A Genetic Algorithm Approach to Image Reconstruction in Electrical Impedance Tomography. IEEE Transactions on Evolutionary Computation 4(1), 83–88 (2000)CrossRefGoogle Scholar
  5. 5.
    Li, Y., Rao, L., He, R., Xu, G., Wu, Q., Yan, W., Dong, G., Yang, Q.: A Novel Combination Method of Electrical Impedance Tomography Inverse Problem for Brain Imaging. IEEE Transactions on Magnetics 41(5), 1848–1851 (1848)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. Neural Networks. In: Proc. IEEE Inter. Conf. on Neural Networks, Perth, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Engelbrecht, A.P., Ismail, A.: Training product unit neural networks. Stability Control: Theory Appl. 2(1-2), 59–74 (1999)Google Scholar
  8. 8.
    Parsopoulos, K.E., Vrahatis, M.N.: On the Computation of All Global Minimizers Through Particle Swarm Optimization. IEEE Trans. Evol. Comput. 8(3), 211–224 (2004)CrossRefGoogle Scholar
  9. 9.
    Chen, M., Wu, C., Fleming, P.J.: An evolutionary particle swarm algorithm for multi-objective optimization. In: Processing of the 7th World Congress on Intelligent Control and Automation, pp. 3269–3274. IEEE Press, Los Alamitos (2008)Google Scholar
  10. 10.
    Linkens, D.A., Chen, M.: Hierarchical Fuzzy Clustering Based on Self-organising Networks. In: Proceedings of World Congress on Computational Intelligence (WCCI 1998), vol. 2, pp. 1406–1410. IEEE, Piscataway (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gang Hu
    • 1
  • Min-you Chen
    • 1
  • Wei He
    • 1
  • Jin-qian Zhai
    • 1
  1. 1.State Key Laboratory of Power Transmission Equipment & System Security and New TechnologyChongqing UniversityChina

Personalised recommendations