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Image Reconstruction Using Genetic Algorithm in Electrical Impedance Tomography

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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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 a genetic algorithm technique for the solution of the static EIT inverse problem. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton–Raphson algorithm at the expense of increased computational burden.

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Electronic Engineering, Cheju National University, Jeju, Jeju-do, 690-756, Korea

    Ho-Chan Kim, Chang-Jin Boo & Min-Jae Kang

Authors
  1. Ho-Chan Kim
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  2. Chang-Jin Boo
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  3. Min-Jae Kang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Kim, HC., Boo, CJ., Kang, MJ. (2006). Image Reconstruction Using Genetic Algorithm in Electrical Impedance Tomography. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_103

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  • DOI: https://doi.org/10.1007/11893295_103

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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