Abstract
In this paper, a framework for calibrating and correcting geometric parameters of CBCT based on visual imaging model is designed. Based on this framework, an automatic detection and recognition method for markers and a calibration and correction method for geometric parameters of CBCT under complex conditions are designed. Specifically, an algorithm for detecting and recognizing markers with high robustness and accuracy to rotation, illumination and marking imaging deformation is proposed. The geometric parameters of visual imaging model are estimated by using the corresponding relationship of high-precision markers, and the geometric parameters are optimized and corrected based on the minimum re-projection error.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zarepisheh, M., Long, T., Li, N., et al.: A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. Med. Phys. 41(6), 061711 (2014)
Yu, G., Liang, Y., Yang, G., et al.: Accelerated gradient-based free form deformable registration for online adaptive radiotherapy. Phys. Med. Biol. 60(7), 2765–2783 (2015)
Usui, K., Ichimaru, Y., Okumura, Y., et al.: Dose calculation with a cone beam CT image in image-guided radiation therapy. Radiol. Phys. Technol. 6(1), 107–114 (2013)
Meng, Y., Gong, H., Yang, X.: Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects. IEEE Trans. Med. Imaging 32(2), 278–288 (2013)
Xu, J., Tsui, B.M.W.: An analytical geometric calibration method for circular cone-beam geometry. IEEE Trans. Med. Imaging 32(9), 1731–1744 (2013)
Zhang, F., Du, J., Jiang, H., et al.: Iterative geometric calibration in circular cone-beam computed tomography. Int. J. Light Electron Opt. 125(11), 2509–2514 (2014)
Schneider, C.-T.: Mehrbildzuordnung. Dimension, Optische Formerfassung, pp. 61–67. DGZfP, Berlin (1991)
Xia, R.B., Zhao, J.B., Liu, W.J., Wu, J.H., Fu, S.P., Jiang, J., Li, J.Z.: A robust recognition algorithm for encoded targets in close-range photogrammetry. J. Inf. Sci. Eng. 27(6), 197–209 (2011)
Matei, B., Meer, P.: A general method for errors-in-variables problems in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (2000)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61703436), and the Fundamental Research Funds for the Central Universities (3332018103).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wan, Y., Chen, Q., Lei, X., Wang, Y., Chen, Y., Hu, H. (2020). A Calibration Method of CBCT Geometric Parameters Based on the Visual Imaging Model. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_81
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)