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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 7419307, 14 pages
Research Article

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

1School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2Lanzhou Yuxin Information Technology Limited Liability Company, Lanzhou 730000, China
3College of Electrical & Information Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China

Received 13 August 2016; Accepted 2 November 2016

Academic Editor: Kostas Marias

Copyright © 2016 Xiaogang Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).