带有几何形变的变形图像配准

Liu Yu; Zhu Bo

doi:10.1631/FITEE.1500045

Your Location：

Home >

Browse articles >

带有几何形变的变形图像配准

计算机与自动化 | Updated：2022-06-06

- 带有几何形变的变形图像配准
- Deformable image registration with geometric changes
- 信息与电子工程前沿（英文） 2015年16卷第10期页码：829-837
- Affiliations：
  
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/FITEE.1500045
  中图分类号：
- 收稿：2015-02-05，
  
  修回：2015-09-09，
  
  录用：2015-06-01，
  
  纸质出版：2015-10
- Accepted：
Scan QR Code
带有几何形变的变形图像配准[J]. 信息与电子工程前沿（英文）, 2015,16(10):829-837.

Liu Yu, Zhu Bo. Deformable image registration with geometric changes[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(10): 829-837.
带有几何形变的变形图像配准[J]. 信息与电子工程前沿（英文）, 2015,16(10):829-837. DOI： 10.1631/FITEE.1500045.

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

R Bajcsy , C Broit . Matching of deformed images . 1982 . Proc. 6th Int. Conf. on Pattern Recognition . 351 - 353 . .

A Beck , M Teboulle . A fast iterative shrinkage-thresholding algorithm for linear inverse problems . SIAM J Imag Sci , 2008 . 2 ( 1 ): 183 - 202 . DOI: 10.1137/080716542 http://doi.org/10.1137/080716542 .

MF Beg , MI Miller , A Trouvé , 等 . Computing large deformation metric mappings via geodesic ﬂows of diffeomorphisms . Int J Comput Vis , 2005 . 61 ( 2 ): 139 - 157 . DOI: 10.1023/B:VISI.0000043755.93987.aa http://doi.org/10.1023/B:VISI.0000043755.93987.aa .

A Chambolle . An algorithm for total variation minimization and applications . J Math Imag Vis , 2004 . 20 ( 1 ): 89 - 97 . DOI: 10.1023/B:JMIV.0000011325.36760.1e http://doi.org/10.1023/B:JMIV.0000011325.36760.1e .

GE Christensen , HJ Johnson . Consistent image registration . IEEE Trans Med Imag , 2001 . 20 ( 7 ): 568 - 582 . DOI: 10.1109/42.932742 http://doi.org/10.1109/42.932742 .

GE Christensen , RD Rabbitt , MI Miller . Deformable templates using large deformation kinematics . IEEE Trans Image Process , 1996 . 5 ( 10 ): 1435 - 1447 . DOI: 10.1109/83.536892 http://doi.org/10.1109/83.536892 .

EL Hall . Computer Image Processing and Recognition , 1979 . Academic Press, New York, USA , .

M Herbin , A Venot , JY Devaux , 等 . Automated registration of dissimilar images: application to medical imagery . Comput Vis Graph Image Process , 1989 . 47 ( 1 ): 77 - 88 . DOI: 10.1016/0734-189X(89)90055-8 http://doi.org/10.1016/0734-189X(89)90055-8 .

M Hernandez , S Olmos , X Pennec . Comparing algorithms for diffeomorphic registration: stationary LDDMM and diffeomorphic demons . 2008 . Proc. 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy . 24 - 35 . .

BD Lucas , T Kanade . An iterative image registration technique with an application to stereo vision . 1981 . Proc. 7th Int. Joint Conf. on Artiﬁcial Intelligence . 121 - 130 . .

J Luck , C Little , W Hoff . Registration of range data using a hybrid simulated annealing and iterative closest point algorithm . 2000 . Proc. IEEE Int. Conf. on Robotics and Automation . 3739 - 3744 . DOI: 10.1109/ROBOT.2000.845314 http://doi.org/10.1109/ROBOT.2000.845314 .

M Niethammer , GL Hart , DF Pace , 等 . Geometric metamorphosis . 2011 . Proc. 14th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention . 639 - 646 . DOI: 10.1007/978-3-642-23629-7_78 http://doi.org/10.1007/978-3-642-23629-7_78 .

FJP Richard , AMM Samson . Metropolis-Hasting techniques for ﬁnite-element-based registration . 2007 . Proc. IEEE Conf. on Computer Vision and Pattern Recognition . 1 - 6 . DOI: 10.1109/CVPR.2007.383422 http://doi.org/10.1109/CVPR.2007.383422 .

LI Rudin , S Osher , E Fatemi . Nonlinear total variation based noise removal algorithms . Phys D , 1992 . 60 ( 1-4 ): 259 - 268 . DOI: 10.1016/0167-2789(92)90242-F http://doi.org/10.1016/0167-2789(92)90242-F .

A Trouvé , L Younes . Metamorphoses through Lie group action . Found Comput Math , 2005 . 5 ( 2 ): 173 - 198 . DOI: 10.1007/s10208-004-0128-z http://doi.org/10.1007/s10208-004-0128-z .

M Zhang , N Singh , PT Fletcher . Bayesian estimation of regularization and atlas building in diffeomorphic image registration . 2013 . Proc. 23rd Int. Conf. on Information Processing in Medical Imaging. p.37-48 . DOI: 10.1007/978-3-642-38868-2_4 http://doi.org/10.1007/978-3-642-38868-2_4 .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor