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Registration of Brain Tumor Images Using Hyper-Elastic Regularization

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Computational Biomechanics for Medicine

Abstract

In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems.

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Notes

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Acknowledgments

This work was partially supported by TUBA-GEBIP (Turkish Academy of Sciences) and EU FP7 Grant No: PIRG03-GA-2008-231052.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

    Andac Hamamci & Gozde Unal

Authors
  1. Andac Hamamci
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  2. Gozde Unal
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Corresponding author

Correspondence to Gozde Unal .

Editor information

Editors and Affiliations

  1. School of Mechanical & Chem. Engineering, Intelligent Systems for Medicine Lab., The University of Western Australia, Stirling Highway 35, Crawley-Perth, 6009, West Australia, Australia

    Adam Wittek

  2. School of Mechanical & Chem. Engineering, The University of Western Australia, Stirling Highway 35, Crawley-Perth, 6009, West Australia, Australia

    Karol Miller

  3. , Auckland Bioengineering Institute, The University of Auckland, Symonds Street 70, Auckland, 1010, New Zealand

    Poul M.F. Nielsen

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Hamamci, A., Unal, G. (2013). Registration of Brain Tumor Images Using Hyper-Elastic Regularization. In: Wittek, A., Miller, K., Nielsen, P. (eds) Computational Biomechanics for Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6351-1_10

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  • DOI: https://doi.org/10.1007/978-1-4614-6351-1_10

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6350-4

  • Online ISBN: 978-1-4614-6351-1

  • eBook Packages: EngineeringEngineering (R0)

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