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Stable Learning for Neural Network Tomography by Using Back Projected Image

  • Conference paper
Book cover Distributed Computing and Artificial Intelligence, 11th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 290))

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Abstract

This paper presents a stable learning method of the neural network tomography, in case of asymmetrical few view projection. The neural network collocation method (NNCM) is one of effective reconstruction tools for symmetrical few view tomography. But in case of asymmetrical few view, the NNCM tends to unstable and fails to reconstruct appropriate tomographic images. We solve the unstable problem by introducing the back projected image in the early learning stage of NNCM. The numerical simulation with an assumed tomographic image show the effectiveness of the proposed method.

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References

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Author information

Authors and Affiliations

  1. Department of Information Systems and Management, Faculty of Applied Information Science, Hiroshima Institute of Technology, 2-1-1 Miyake, Saeki-ku, Hiroshima, 731-5193, Japan

    Masaru Teranishi, Keita Oka & Masahiro Aramoto

Authors
  1. Masaru Teranishi
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  2. Keita Oka
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  3. Masahiro Aramoto
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Corresponding author

Correspondence to Masaru Teranishi .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Université Libre de Bruxelles, Bruxelles, Belgium

    Hugues Bersini

  3. Dept. of Computing Science and Control Faculty of Science, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

  4. Department of Computing Science and Control, Faculty of Science, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  5. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Paweł Pawlewski

  6. Dep. PPEQS, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Italy

    Edgardo Bucciarelli

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© 2014 Springer International Publishing Switzerland

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Teranishi, M., Oka, K., Aramoto, M. (2014). Stable Learning for Neural Network Tomography by Using Back Projected Image. In: Omatu, S., Bersini, H., Corchado, J., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-07593-8_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07592-1

  • Online ISBN: 978-3-319-07593-8

  • eBook Packages: EngineeringEngineering (R0)

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