Deformable image registration with geometric changes

Liu Yu; Zhu Bo

doi:10.1631/FITEE.1500045

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Deformable image registration with geometric changes

Computer & Automation | Updated：2022-06-06

- Deformable image registration with geometric changes
- 带有几何形变的变形图像配准
- Frontiers of Information Technology & Electronic Engineering Vol. 16, Issue 10, Pages: 829-837(2015)
- Affiliations：
  
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/FITEE.1500045
  CLC：
- Received：05 February 2015，
  
  Revised：2015-09-09，
  
  Accepted：01 June 2015，
  
  Published：2015-10
- Accepted：
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Liu Yu, Zhu Bo. Deformable image registration with geometric changes[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(10): 829-837.
DOI：

Liu Yu, Zhu Bo. Deformable image registration with geometric changes[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(10): 829-837. DOI： 10.1631/FITEE.1500045.

摘要

目的

几何形态的变化为变形图像配准带来了许多障碍。本文提出一个用以描述几何形变的数学模型，可以在变形图像配准中实现源图像与目标图像之间的平滑变换。

创新点

提出一个新的图像配准模型，可以显著抑制局部几何形变对图像配准的影响并极大地提高配准准确性。

方法

本文提出的配准模型中主要引入一个可以将几何形变区域正则化的L1范数。这一稀疏诱导范数可以通过抑制局部变换来实现平滑的全局变换。为保证算法的稳定性和快速收敛，文本对算法的数值解进行了详细讨论。

结论

通过将算法应用于真实采集的外伤性脑损伤图像，验证了算法的实用性和有效性。实验显示使用本文所提算法对目标图像进行的重建比使用普通的弹性配准模型具有更高的准确性。

Abstract

Geometric changes present a number of difficulties in deformable image registration. In this paper

we propose a global deformation framework to model geometric changes whilst promoting a smooth transformation between source and target images. To achieve this

we have developed an innovative model which significantly reduces the side effects of geometric changes in image registration

and thus improves the registration accuracy. Our key contribution is the introduction of a sparsity-inducing norm

which is typically L1 norm regularization targeting regions where geometric changes occur. This preserves the smoothness of global transformation by eliminating local transformation under different conditions. Numerical solutions are discussed and analyzed to guarantee the stability and fast convergence of our algorithm. To demonstrate the effectiveness and utility of this method

we evaluate it on both synthetic data and real data from traumatic brain injury (TBI). We show that the transformation estimated from our model is able to reconstruct the target image with lower instances of error than a standard elastic registration model.

关键词

Keywords

references

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