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Intelligent Optimization Algorithm Approach to Image Reconstruction in Electrical Impedance Tomography

  • Conference paper
Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

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Abstract

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents two intelligent optimization algorithm techniques such as genetic algorithm (GA) and simulated annealing (SA) for the solution of the static EIT inverse problem. We summarize the simulation results for the modified Newton-Raphson, GA, and SA algorithms.

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References

  1. Webster, J.G.: Electrical Impedance Tomography, Adam Hilger (1990)

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  2. Yorkey, T.J., Webster, J.G., Tompkins, W.J.: Comparing Reconstruction Algorithms for Electrical Impedance Tomography. IEEE Trans. on Biomedical Eng. 34, 843–852 (1987)

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  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)

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  4. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)

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  5. Kim, M.C., Kim, S., Kim, K.Y., Lee, J.H., Lee, Y.J.: Reconstruction of Particle Concentration Distribution in Annular Couette Flow Using Electrical Impedance Tomography. J. Ind. Eng. Chem. 7, 341–347 (2001)

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Author information

Authors and Affiliations

  1. Dept. of Electrical Eng., Cheju National Univ., Cheju, 690-756, Korea

    Ho-Chan Kim & Chang-Jin Boo

Authors
  1. Ho-Chan Kim
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  2. Chang-Jin Boo
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Kim, HC., Boo, CJ. (2006). Intelligent Optimization Algorithm Approach to Image Reconstruction in Electrical Impedance Tomography. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_113

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  • DOI: https://doi.org/10.1007/11881070_113

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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