Image Registration Method Based on Distributed Alternating Direction Multipliers

IF 1.6 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Huizhong Ji, Zhili Zhang, Peng Xue, Meirong Ren, Enqing Dong
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引用次数: 0

Abstract

Purpose

Image registration is a critical component in medical image analysis applications. Optimization algorithms for energy functions play a crucial role in registration. Most registration methods improve the performance by modifying the energy function and optimizing it directly, neglecting the impact of the optimization algorithm. This paper is to investigate how to efficiently design an attention allocation strategy and improve the convergence of the optimization algorithm.

Methods

This paper introduces a novel image registration method that leverages the distributed alternating direction method of multipliers to perform optimization, named DADMMreg. Compared to the optimization algorithm using the alternating direction method of multipliers (ADMM), the optimization algorithm used in DADMMreg achieves better convergence by altering the optimization order of the similarity and regularization terms within the energy function. To overcome the limitations of intensity-based or structural-based similarity metrics, a modified structural similarity measure (SSIM) is proposed that takes into account both intensity and structural information. Considering that homogeneous smoothing prior at the sliding surface leads to inaccurate registration, a novel vector-modulus-based regularization metric is proposed to avoid physically implausible displacement fields.

Results

Experimental results on 4D-CT image dataset and COPD image dataset demonstrate the satisfactory registration performance of DADMMreg, with an average target registration error (TRE) of 0.9105 mm and 0.9201 mm, respectively. Meanwhile, the experimental results show that the DADMMreg method exhibits better convergence performance than other registration methods.

Conclusion

Compared to classical methods, the attention allocation strategy of DADMMreg enables faster convergence with comparable registration accuracy.

Abstract Image

基于分布式交替方向乘法器的图像注册方法
目的图像配准是医学图像分析应用中的一个重要组成部分。能量函数的优化算法在配准中起着至关重要的作用。大多数配准方法都是通过修改能量函数并直接对其进行优化来提高性能,忽略了优化算法的影响。本文旨在研究如何有效设计注意力分配策略,提高优化算法的收敛性。方法本文介绍了一种新型图像配准方法,该方法利用分布式交替方向乘法进行优化,命名为 DADMMreg。与使用交替乘数方向法(ADMM)的优化算法相比,DADMMreg 中使用的优化算法通过改变能量函数中相似性项和正则化项的优化顺序实现了更好的收敛性。为了克服基于强度或基于结构的相似性度量的局限性,提出了一种同时考虑强度和结构信息的修正结构相似性度量(SSIM)。结果在 4D-CT 图像数据集和 COPD 图像数据集上的实验结果表明,DADMMreg 的配准性能令人满意,平均目标配准误差(TRE)分别为 0.9105 mm 和 0.9201 mm。同时,实验结果表明 DADMMreg 方法比其他配准方法具有更好的收敛性能。
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来源期刊
CiteScore
4.30
自引率
5.00%
发文量
81
审稿时长
3 months
期刊介绍: The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
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