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Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks

Author

Listed:
  • Chenwei Cai

    (Department of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, China)

  • Lvda Wang

    (Beijing Institute of Computer Technology and Applications, Beijing 100036, China)

  • Shihui Ying

    (Department of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, China)

Abstract

Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.

Suggested Citation

  • Chenwei Cai & Lvda Wang & Shihui Ying, 2022. "Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks," Mathematics, MDPI, vol. 10(11), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1946-:d:832542
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    References listed on IDEAS

    as
    1. Shihui Ying & Dan Li & Bin Xiao & Yaxin Peng & Shaoyi Du & Meifeng Xu, 2017. "Nonlinear image registration with bidirectional metric and reciprocal regularization," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
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