| Title: | Arbitrary Order Total Variation for Deformable Image Registration |
| Journal: | Pattern Recognition |
| Published: | 1 May 2023 |
| DOI: | https://doi.org/10.1016/j.patcog.2023.109318 |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 7850
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Abstract
In this work, we investigate image registration in a variational framework and focus on regularization generality and solver efficiency. We first propose a variational model combining the state-of-the-art sum of absolute differences (SAD) and a new arbitrary order total variation regularization term. The main advantage is that this variational model preserves discontinuities in the resultant deformation while being robust to outlier noise. It is however non-trivial to optimize the model due to its non-convexity, non-differentiabilities, and generality in the derivative order. To tackle these, we propose to first apply linearization to the model to formulate a convex objective function and then break down the resultant convex optimization into several point-wise, closed-form subproblems using a fast, over-relaxed alternating direction method of multipliers (ADMM). With this proposed algorithm, we show that solving higher-order variational formulations is similar to solving their lower-order counterparts. Extensive experiments show that our ADMM is significantly more efficient than both the subgradient and primal-dual algorithms particularly when higher-order derivatives are used, and that our new models outperform state-of-the-art methods based on deep learning and free-form deformation. Our code implemented in both Matlab and Pytorch is publicly available at https://github.com/j-duan/AOTV.5 Authors
- Jinming Duan
- Xi Jia
- Joseph Bartlett
- Wenqi Lu
- Zhaowen Qiu
1 Application
| Application ID | Title |
|---|---|
| 40119 | SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning |
Enabling scientific discoveries that improve human health