Journal of System Simulation
Abstract
Abstract: Aiming at the of large amount of computational data and low registration efficiency in 3D cranial medical image registration, a fast registration method based on geometric feature space constraints is proposed. The algorithm extracts three-dimensional contour point clusters, and proposes a feature construction method based on the optimal fitting ring of point clusters. The feature rings and the centroids of each layer are used as feature quantities, and the fast registration is completed by using Iterative Closest Point (ICP) method. The experimental results show that the method has less computation amount, high satisfactory registration accuracy and much faster registration speed than the traditional ICP algorithm. It is an effective real-time three-dimensional registration method.
Recommended Citation
Gu, Juping; Cheng, Tianyu; Wang, Jianping; Liang, Hua; Zhao, Fengshen; and Ling, Jiang
(2020)
"Fast 3D Medical Image Registration Based on Geometric Feature Invariants,"
Journal of System Simulation: Vol. 32:
Iss.
11, Article 5.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0402E
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss11/5
First Page
2105
Revised Date
2019-08-02
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0402E
Last Page
2111
CLC
TP301.6
Recommended Citation
Gu Juping, Cheng Tianyu, Wang Jianping, Hua Liang, Zhao Fengshen, Jiang Ling. Fast 3D Medical Image Registration Based on Geometric Feature Invariants[J]. Journal of System Simulation, 2020, 32(11): 2105-2111.
DOI
10.16182/j.issn1004731x.joss.19-FZ0402E
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