2D/3D Hierarchical Registration Based on Principal Direction Fourier Transform Operator

被引：0
|
作者：
Yang K. [1 ,2 ,3 ]
Luo Y. [1 ,2 ]
Zhao Y. [1 ,2 ]
Zhao X. [1 ,2 ]
Song G. [1 ,2 ]
机构：
[1] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang
[2] Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang
[3] University of Chinese Academy of Sciences, Beijing
来源：
Jiqiren/Robot | 2021年 / 43卷 / 03期
关键词：
2D/3D registration; Fourier transform; Moment; Principal direction; Registration framework;
D O I：
10.13973/j.cnki.robot.200425
中图分类号：
学科分类号：
摘要：
This paper aims to solve the problems of the contradiction between the registration accuracy and the efficiency, and also the small registration capture range in the current 2D/3D medical image registration research. A hierarchical registration method based on principal direction Fourier transform operator (PDFTO) is proposed. Firstly, an operator with invariance of in-plane rotation and translation, PDFTO, is proposed. Then, a PDFTO-based template matching initialization method is proposed, which can avoid the requirement of an initial value that should be close to the true value, and can significantly expand the capture range. Finally, a hierarchical registration framework based on PDFTO is proposed, which reduces the searching space of registration from O(n6) to O(n2), and greatly improves the efficiency of registration while ensuring the accuracy of registration. In the registration experiments, the registration accuracy of the proposed method is 0.68 mm ± 0.28 mm, the registration time is 16.87 s ± 3.77 s, and the capture range is larger than 100 mm. Therefore, the proposed PDFTO-based hierarchical registration method can meet the requirements for the accuracy, the efficiency and the capture range in 2D/3D image registration in related clinical applications. © 2021, Science Press. All right reserved.
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页码：296 / 307
页数：11
相关论文
共 20 条
  • [1] Tang T S Y, Ellis R E, Fichtinger G., Fiducial registration from a single X-ray image: A new technique for fluoroscopic guidance and radiotherapy, Lecture Notes in Computer Science, 1935, pp. 502-511, (2000)
  • [2] Wang J, Schaffert R, Borsdorf A, Et al., Dynamic 2-D/3-D rigid registration framework using point-to-plane correspondence model, IEEE Transactions on Medical Imaging, 36, 9, pp. 1939-1954, (2017)
  • [3] Livyatan H, Yaniv Z, Joskowicz L., Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT, IEEE Transactions on Medical Imaging, 22, 11, pp. 1395-1406, (2003)
  • [4] Chen Z Q, Wang Z W, Fang L W, Et al., Real-time 2D/3D registration of vertebra via machine learning and geometric transformation, Acta Automatica Sinica, 44, 7, pp. 1183-1194, (2018)
  • [5] Meng C, Wang Q, Guan S Y, Et al., Weighted local mutual information for 2D-3D registration in vascular interventions, Lecture Notes in Computer Science, 11004, pp. 376-385, (2018)
  • [6] Meng C, Wang Q, Guan S Y, Et al., 2D-3D registration with weighted local mutual information in vascular interventions, IEEE Access, 7, pp. 162629-162638, (2019)
  • [7] Wang Y., 2D/3D multi-modal medical image registration based on iterative regression, (2020)
  • [8] Zhang R, Wang L, Xia W, Et al., Comparison of similarity measurement and optimization methods in 2D/3D image registration, Laser & Infrared, 44, 1, pp. 98-102, (2014)
  • [9] Gui P, Ling W K, Zhang D Y, Et al., Cross-cumulative residual entropy-based medical image registration via hybrid differential search algorithm, International Journal of Imaging Systems and Technology, 29, 4, pp. 701-710, (2019)
  • [10] Chen S J, Shen H L, Li C G, Et al., Normalized total gradient: A new measure for multispectral image registration, IEEE Transactions on Image Processing, 27, 3, pp. 1297-1310, (2017)
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