An Efficient Numerical Method for Mean Curvature-Based Image Registration Model

Author:

Zhang Jin,Chen Ke,Chen Fang,Yu Bo

Abstract

AbstractMean curvature-based image registration model firstly proposed by Chumchob-Chen-Brito (2011) offered a better regularizer technique for both smooth and nonsmooth deformation fields. However, it is extremely challenging to solve efficiently this model and the existing methods are slow or become efficient only with strong assumptions on the smoothing parameterβ. In this paper, we take a different solution approach. Firstly, we discretize the joint energy functional, following an idea of relaxed fixed point is implemented and combine with Gauss-Newton scheme with Armijo's Linear Search for solving the discretized mean curvature model and further to combine with a multilevel method to achieve fast convergence. Numerical experiments not only confirm that our proposed method is efficient and stable, but also it can give more satisfying registration results according to image quality.

Publisher

Global Science Press

Subject

Applied Mathematics

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multiscale Residual Solver for Total Variation Models;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08

2. Computing Curvature, Mean Curvature and Weighted Mean Curvature;2022 IEEE International Conference on Image Processing (ICIP);2022-10-16

3. Constrained Linear Curvature Image Registration Model and Its Numerical Algorithm;Mathematical Problems in Engineering;2020-10-12

4. Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity;KSII Transactions on Internet and Information Systems;2020-03-31

5. Weighted mean curvature;Signal Processing;2019-11

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