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Annual Conference on Medical Image Understanding and Analysis

MIUA 2019: Medical Image Understanding and Analysis pp 339–350Cite as

High-Order Markov Random Field Based Image Registration for Pulmonary CT

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1065))

Abstract

Deformable image registration is an important tool in medical image analysis. In the case of lung four dimensions computed tomography (4D CT) registration, there is a major problem that the traditional image registration methods based on continuous optimization are easy to fall into the local optimal solution and lead to serious misregistration. In this study, we proposed a novel image registration method based on high-order Markov Random Fields (MRF). By analyzing the effect of the deformation field constraint of the potential functions with different order cliques in MRF model, energy functions with high-order cliques form are designed separately for 2D and 3D images to preserve the deformation field topology. For the complexity of the designed energy function with high-order cliques form, the Markov Chain Monte Carlo (MCMC) algorithm is used to solve the optimization problem of the designed energy function. To address the high computational requirements in lung 4D CT image registration, a multi-level processing strategy is adopted to reduce the space complexity of the proposed registration method and promote the computational efficiency. Compared with some other registration methods, the proposed method achieved the optimal average target registration error (TRE) of 0.93 mm on public DIR-lab dataset with 4D CT images, which indicates its great potential in lung motion modeling and image guided radiotherapy.

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Notes

  1. 1.

    https://www.dir-lab.com/index.html.

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Acknowledgements

This paper is supported by the Fundamental Research Funds for the Central Universities (China), the National Natural Science Foundation of China (Nos. 81671848 and 81371635) and Shandong Key Research and Development Program (2019GGX101022).

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Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China

    Peng Xue, Enqing Dong & Huizhong Ji

Authors
  1. Peng Xue
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  2. Enqing Dong
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  3. Huizhong Ji
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Corresponding author

Correspondence to Enqing Dong .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Yalin Zheng

  2. University of Liverpool, Liverpool, UK

    Bryan M. Williams

  3. University of Liverpool, Liverpool, UK

    Ke Chen

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Xue, P., Dong, E., Ji, H. (2020). High-Order Markov Random Field Based Image Registration for Pulmonary CT. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_29

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  • DOI: https://doi.org/10.1007/978-3-030-39343-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39342-7

  • Online ISBN: 978-3-030-39343-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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