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GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application

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Scale Space and Variational Methods in Computer Vision (SSVM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12679))

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Abstract

In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentation role. The new SRS model is much simpler comparing with the models derived from the hidden Markov measure field model (HMMFM). Numerical results show that the proposed model achieves improved results than other methods, and the CPU time is greatly reduced.

The work was supported by Villum Investigator grant 25893 from the Villum Foundation.

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Author information

Authors and Affiliations

  1. School of Mathematical Science, Nankai University, Tianjin, 300071, China

    Shi Yan

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark

    Shi Yan & Yiqiu Dong

Authors
  1. Shi Yan
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  2. Yiqiu Dong
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Corresponding author

Correspondence to Shi Yan .

Editor information

Editors and Affiliations

  1. UNICAEN, GREYC – Normandy University, Caen, France

    Abderrahim Elmoataz

  2. ENSICAEN, GREYC – Normandy University, Caen, France

    Jalal Fadili

  3. CNRS, GREYC – Normandy University, Caen, France

    Yvain Quéau

  4. UNICAEN, GREYC – Normandy University, Caen, France

    Julien Rabin

  5. ENSICAEN, GREYC – Normandy University, Caen, France

    Loïc Simon

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Yan, S., Dong, Y. (2021). GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT Application. In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., Simon, L. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science(), vol 12679. Springer, Cham. https://doi.org/10.1007/978-3-030-75549-2_40

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  • DOI: https://doi.org/10.1007/978-3-030-75549-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75548-5

  • Online ISBN: 978-3-030-75549-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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