Skip to main content
SpringerLink
Log in

Registration of Structured Light Camera Point Cloud Data with CT Images

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14267))

Included in the following conference series:

  • 468 Accesses

Abstract

With the advancement of structured-light cameras, surgical robots equipped with such cameras have been utilized for lesion localization during surgeries. Achieving precise registration between CT images and point cloud data remains a challenge. This study proposes a registration method for CT images and point cloud data. Firstly, the CT images are converted into a point cloud representation, and Feature Histograms (FPFH) are computed based on the point cloud's normal vectors. Subsequently, the Fast Global Registration (FGR) algorithm is employed to perform coarse registration of the point cloud. Finally, the Iterative Closest Point (ICP) algorithm is utilized for fine registration of the point cloud data. Experimental evaluation is conducted using CT images of a human brain model and point cloud data obtained from a structured-light camera. The results demonstrate a favorable registration performance. The coarse registration facilitated by the FGR algorithm serves as an effective initialization for the ICP algorithm, thereby enhancing the convergence speed and accuracy of the fine registration process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Besl, P.J.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (2016)

    Article  Google Scholar 

  2. Jie, D.: Multi-label classification feature selection algorithm based on neighborhood rough sets. Comput. Res. Develop. 52(1), 56–65 (2015)

    MathSciNet  Google Scholar 

  3. Lei, Z.: Constrained improved ICP point cloud registration method. Comput. Eng. Appl. 18, 197–200 (2012)

    Google Scholar 

  4. Tamaki, T.: Softassign and EM-ICP on GPU. IEEE Int. Conf. Network. Comput. 2(3), 179–183 (2010)

    Google Scholar 

  5. Myronenko, A.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)

    Article  Google Scholar 

  6. Wang, Q.: Point cloud registration algorithm based on NDT and ICP. Comput. Eng. Appl. 56(07), 88–95 (2020)

    Google Scholar 

  7. Li, X.: 2020 Point cloud registration based on neighborhood feature point extraction and matching. Acta Photonica Sinica 49(04), 255–265 (2020)

    Google Scholar 

  8. Zhao, M.: Point cloud registration method integrating sampling consistency and iterative nearest point algorithm. J. Laser Appl. 40(10), 45–50 (2019)

    Google Scholar 

  9. Huang, J.: Point cloud registration algorithm base on ICP algorithm and 3D-NDT algorithm. Int. J. Wireless Mobile Comput. 22(2), 125–130 (2022)

    Article  MathSciNet  Google Scholar 

  10. Zhang, Z.: An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty. Int. J. Med. Robot. Comput. Assist. Surg. 19(1), 26–38 (2023)

    Google Scholar 

  11. Wang, H.: Deep-Learning-Based multiview RGBD sensor system for 3-D face point cloud registration. IEEE Sensor Let. 7(5), 1–4 (2023)

    Article  Google Scholar 

Download references

Acknowledgments

This research was Supported by the Joint Funds of Guangdong Basic and Applied Basic Research Foundation (2019A1515110261), National Natural Science Foundation of China (Grant No. 52105009), Shenzhen Science and Technology Plan Project (Grant No. KCXFZ20201221173202007), the Key Scientific Research Platforms and Projects of Guangdong Regular Institutions of Higher Education, China, under Grant 2022KCXTD033, the Scientific Research Capacity Improvement Project of Key Developing Disciplines in Guangdong Province, China, under Grant 2021ZDJS084, the Guangdong Natural Science Foundation, China, under Grant 2023A1515012103, the Key Laboratory of Robotics and Intelligent Equipment of Guangdong Regular Institutions of Higher Education, China, under Grant 2017KSYS009, the Innovation Center of Robotics and Intelligent Equipment, China, under Grant KCYCXPT2017006, the Special Projects in Key Fields from the Department of Education of Guangdong Province (2022ZDZX2059), the Dongguan Science and Technology of Social Development Program (20221800905072).

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China

    Wencong Chen, Juqing Song & Shuai Wang

  2. Department of Neurosurgery, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518052, China

    Qinghua Zhang

  3. The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, 518000, China

    Qinghua Zhang

Authors
  1. Wencong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juqing Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qinghua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinghua Zhang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

  4. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  5. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, W., Song, J., Wang, S., Zhang, Q. (2023). Registration of Structured Light Camera Point Cloud Data with CT Images. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_50

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6483-3_50

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6482-6

  • Online ISBN: 978-981-99-6483-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation