Robust and Accurate Nonrigid Point Set Registration Algorithm to Accommodate Anisotropic Positional Localization Error Based on Coherent Point Drift

Min，Zhe1; Meng，Max Q.H.2

2020

10.1109/TASE.2020.3027073

IEEE Transactions on Automation Science and Engineering   影响因子和分区

1545-5955

1558-3783

Nonrigid point set (PS) registration is an outstanding and fundamental problem in the fields of robotics, computer vision, medical image analysis, and image-guided surgery (IGS). The aim of a nonrigid registration problem is to align together two point sets where one has been deformed. The assumption of isotropic localization error is shared in the previous nonrigid registration algorithms. In this article, we have derived and presented a novel nonrigid registration algorithm, where the position localization error (PLE) is generalized to be anisotropic, which means that the error distribution is not the same in different spatial directions. The motivation of considering the anisotropic characteristic is that the PLE is actually different in three spatial directions in real applications of registrations, such as IGS. Mathematically, the difficulty in dealing with the anisotropic error case comes from the change from a standard deviation that is a scalar to a covariance matrix. The formulas for updating the parameters in both expectation and maximization steps are derived. More specifically, in the expectation step, we compute the posterior probabilities that represent the correspondences between points in two PSs. In the maximization step, given the current posteriors, the covariance matrix of the PLE and the nonrigid transformation are updated. To further speed up the proposed algorithm, the low-rank approximation variation of our method is also presented. We have demonstrated through experiments on both general and medical data sets (corrupted with noise) that the proposed algorithm outperforms the state-of-the-art ones in terms of registration accuracy and robustness to noise. More specifically, all the experimental results have passed the statistical tests at the 5% significance level.

Biomedical engineering Biomedical imaging Covariance matrices healthcare and life sciences Image reconstruction image-guided surgery (IGS) Kernel Robots Robustness Surgery surgical navigation.

EI

英语

其他

2-s2.0-85092925546

Scopus

1.Robotics, Perception and Artificial Intelligence Laboratory, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China.
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China, and also with the Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518172, China, on leave from the Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong (e-mail: max.meng@ieee.org)

Min，Zhe,Meng，Max Q.H.. Robust and Accurate Nonrigid Point Set Registration Algorithm to Accommodate Anisotropic Positional Localization Error Based on Coherent Point Drift[J]. IEEE Transactions on Automation Science and Engineering,2020,18(4):1939-1955.

Min，Zhe,&Meng，Max Q.H..(2020).Robust and Accurate Nonrigid Point Set Registration Algorithm to Accommodate Anisotropic Positional Localization Error Based on Coherent Point Drift.IEEE Transactions on Automation Science and Engineering,18(4),1939-1955.

Min，Zhe,et al."Robust and Accurate Nonrigid Point Set Registration Algorithm to Accommodate Anisotropic Positional Localization Error Based on Coherent Point Drift".IEEE Transactions on Automation Science and Engineering 18.4(2020):1939-1955.