Hierarchical optimization of skull point cloud registration

被引：0
|
作者：
Yang W. [1 ]
Zhou M.-Q. [1 ,2 ]
Geng G.-H. [1 ]
Liu X.-N. [1 ]
Li K. [1 ]
Zhang H.-B. [1 ]
机构：
[1] College of Information Science and Technology, Northwest University, Xi'an
[2] College of Information Science and Technology, Beijing Normal University, Beijing
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2019年 / 27卷 / 12期
关键词：
Feature points; Geometric feature constraints; Iterative closest point; K-means algorithm; Skull registration;
D O I：
10.3788/OPE.20192712.2730
中图分类号：
学科分类号：
摘要：
Skull registration is one of the most important steps in craniofacial reconstruction. Its accuracy and efficiency have amajor impact on craniofacial reconstruction results. To improve the accuracy and efficiency of skull point cloud registration, this study proposed a hierarchical optimization method for skull point cloud registration. We divided skull registration into two processes, coarse and fine. First, the skull cloud model was denoised, simplified, and normalized. Then, the feature points were extracted from the skull point cloud model and their feature sequences were calculated. The initial corresponding point pairs were constrained based on the feature sequence, and the algorithm was used to eliminate the mismatched points to achieve coarse registration of the skull. Finally, an improved Iterative Closest Point (ICP) algorithm with geometric feature constraints was used to achieve fine skull registration to achieve the goal of accurate skull registration. In this study, experiments on rough, fine, and first coarse and then fine registration were conducted. Results show that in the coarse registration process, the registration accuracy of the optimized coarse registration algorithm is improved by approximately 35%, and the algorithm time consumption is increased by approximately 6% as compared with the unoptimized coarse registration algorithm. In the fine registration process, the registration accuracy and convergence speed of the improved ICP algorithm are improved by approximately 20% and 43%, respectively, and the time consumption of the algorithm is reduced by approximately 47% as compared with the ICP algorithm. For the complete registration process, the registration accuracy and convergence speed of the algorithm are better than those of the other two methods. Therefore, this method is an effective skull point cloud registration algorithm that can achieve accurate registration of a skull point cloud. © 2019, Science Press. All right reserved.
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页码：2730 / 2739
页数：9
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