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BioMed Research International
Volume 2016 (2016), Article ID 4674658, 12 pages
Research Article

DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

1School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
3College of Electrical & Information Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China

Received 7 July 2016; Accepted 30 August 2016

Academic Editor: Andrey Krylov

Copyright © 2016 Zhe Guo 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.


Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.