A new parametric non-rigid image registration method based on Helmholtz's theorem

被引:0
|
作者
Hsiao, Hsi-Yue [1 ]
Chen, Hua-mei [1 ]
Lin, Ting-Hung [1 ]
Hsieh, Chih-Yao [1 ]
Chu, Mei-Yi [1 ]
Liao, Guojun [2 ]
Zhong, Hualiang [3 ]
机构
[1] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
[2] Univ Texas Arlington, Dept Math, Arlington, TX 76019 USA
[3] Virginia Commonwealth Univ, Dept Radiat Oncol, Richmond, VA 23298 USA
关键词
non-rigid image registration; parametric image registration; Helmholtz's theorem; gradient descent optimization; div-curl solver; inverse filtering;
D O I
10.1117/12.770473
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Helmholtz's theorem states that, with suitable boundary condition, a vector field is completely determined if both of its divergence and curl are specified everywhere. Based on this, we developed a new parametric non-rigid image registration algorithm. Instead of the displacements of regular control grid points, the curl and divergence at each grid point are employed as the parameters. The closest related work was done by Kybic where the parameters are the Bspline coefficients of the displacement field at each control grid point. However, in Kybic's work, it is very likely to result in grid folding in the final deformation field if the distance between adjacent control grid points (knot spacing) is less than 8. This implies that the high frequency components in the deformation field can not be accurately estimated. Another relevant work is the NiRuDeGG method where by solving a div-curl system, an intermediate vector field is generated and, in turn, a well-regularized deformation field can be obtained. Though the present work does not guarantee the regularity (no mesh folding) of the resulting deformation field, which is also suffered by Kybic's work, it allows for a more efficient optimization scheme over the NiRuDeGG method. Our experimental results showed that the proposed method is less prone to grid folding than Kybic's work and that in many cases, in a multi-resolution fashion; the knot spacing can be reduced down to I and thus has the potential to achieve higher registration accuracy. Detailed comparison among the three algorithms is described in the paper.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] An Efficient Algorithm for Non-Rigid Image Registration
    Wang, Guanglei
    Lui, Hoi-Shun
    Persson, Mikael
    2010 4TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ICSPCS), 2010,
  • [22] A Stochastic Approach for Non-Rigid Image Registration
    Kolesov, Ivan
    Lee, Jehoon
    Vela, Patricio
    Tannenbaum, Allen
    IMAGE PROCESSING: ALGORITHMS AND SYSTEMS XI, 2013, 8655
  • [23] A Non-rigid Multimodal Image Registration Method Based on Particle Filter and Optical Flow
    Arce-Santana, Edgar
    Campos-Delgado, Daniel U.
    Alba, Alfonso
    ADVANCES IN VISUAL COMPUTING, PT I, 2010, 6453 : 35 - 44
  • [24] Non-rigid image registration by neural computation
    Srikanchana, R
    Xuan, JH
    Freedman, MT
    Nguyen, CC
    Wang, Y
    JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2004, 37 (2-3): : 237 - 246
  • [25] Non-rigid image registration by neural computations
    Srikanchana, R
    Woods, K
    Xuan, JH
    Nguyen, C
    Wang, Y
    NEURAL NETWORKS FOR SIGNAL PROCESSING XI, 2001, : 413 - 422
  • [26] Non-rigid image registration: theory and practice
    Crum, WR
    Hartkens, T
    Hill, DLG
    BRITISH JOURNAL OF RADIOLOGY, 2004, 77 : S140 - S153
  • [27] Non-Rigid Image Registration by Neural Computation
    Rujirutana Srikanchana
    Jianhua Xuan
    Matthew T. Freedman
    Charles C. Nguyen
    Yue Wang
    Journal of VLSI signal processing systems for signal, image and video technology, 2004, 37 : 237 - 246
  • [28] An improved non-rigid image registration approach
    He K.
    Wei Y.
    Wang Y.
    Huang W.-R.
    Gongcheng Kexue Xuebao/Chinese Journal of Engineering, 2019, 41 (07): : 955 - 960
  • [29] A Stochastic Quasi-Newton Method for Non-Rigid Image Registration
    Qiao, Yuchuan
    Sun, Zhuo
    Lelieveldt, Boudewijn P. F.
    Staring, Marius
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT II, 2015, 9350 : 297 - 304
  • [30] A variational Bayesian method for similarity learning in non-rigid image registration
    Grzech, Daniel
    Azampour, Mohammad Farid
    Glocker, Ben
    Schnabel, Julia
    Navab, Nassir
    Kainz, Bernhard
    Le Folgoc, Loic
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 119 - 128