Abstract
Marker-based optical tracking systems (OTS) are widely used in clinical image-guided therapy. However, the emergence of ghost markers, which is caused by the mistaken recognition of markers and the incorrect correspondences between marker projections, may lead to tracking failures for these systems. Therefore, this paper proposes a strategy to prevent the emergence of ghost markers by identifying markers based on the features of their projections, finding the correspondences between marker projections based on the geometric information provided by markers, and fast-tracking markers in a 2D image between frames based on the sizes of their projections. Apart from validating its high robustness, the experimental results show that the proposed strategy can accurately recognize markers, correctly identify their correspondences, and meet the requirements of real-time tracking.















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References
Wagner, T. H. et al., Optical tracking technology in stereotactic radiation therapy. Med. Dosim. 32(2):111–120, 2007.
Meeks, S. L. et al., Optically guided patient positioning techniques. Semin. Radiat. Oncol. 15(3):192–201, 2005.
Peters, T. M., Image-guidance for surgical procedures. Phys. Med. Biol. 51(14):505–540, 2006.
Shamir, R. R., and Joskowicz, L., Geometrical analysis of registration errors in point-based rigid-body registration using invariants. Med. Image Anal. 15(1):85–95, 2011.
Shamir, R. R. et al., Localization and registration accuracy in image guided neurosurgery: a clinical study. Int. J. Comput. Assist. Radiol. Surg. 4(1):45–52, 2009.
Huang, Q. H., Wu, B. W. et al., Fully automatic three-dimensional ultrasound imaging based on conventional B-scan. IEEE T Biomed Cirs S. 12(2):426–436, 2018.
Huang, Q. H., Lan, J. L. et al., Robotic arm based automatic ultrasound scanning for three-dimensional imaging. IEEE Trans on Industrial Informatics 2018:1-1, 2018.
Lin, Q., Cai, K., Yang, R. et al., Geometric calibration of markerless optical surgical navigation system. Int J Med Robotics Comput Assist Surg 15(2):e1978, 2019.
Hong, J., and Hashizume, M., An effective point-based registration tool for surgical navigation. Surg. Endosc. 24(4):944–948, 2010.
Tauscher, S. et al., OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int. J. Comput. Assist. Radiol. Surg. 10(3):285–292, 2015.
Pflugi, S. et al., A Cost-effective surgical navigation solution for periacetabular osteotomy (PAO) Surgery. Int. J. Comput. Assist. Radiol. Surg. 11(2):271–280, 2016.
Chen, X. et al., Development of a surgical navigation system based on augmented reality using an optical see-through head-mounted display. J. Biomed. Inf. 55(C):124–131, 2015.
Pflugi, S. et al., Augmented marker tracking for peri-acetabular osteotomy surgery. Int. J. Comput. Assist. Radiol. Surg. 13(2):291–304, 2018.
Hong, J. et al., Medical navigation system for otologic surgery based on hybrid registration and virtual intraoperative computed tomography. IEEE Trans. Biomed. Eng. 56(2):426–432, 2009.
Lin, Q. et al., Strategy for accurate liver intervention by an optical tracking system. Biomed. Opt. Express. 6(9):3287–3302, 2015.
Soete, G. et al., Initial clinical experience with infrared-reflecting skin markers in the positioning of patients treated by conformal radiotherapy for prostate cancer. Int. J. Radiat. Oncol. Biol. Phys. 52(3):694–698, 2002.
Yan, H. et al., A phantom study on the positioning accuracy of the Novalis body system. Med. Phys. 30:3052–3060, 2003.
Yan, H., Zhu, G. et al., The investigation on the location effect of external markers in respiratory-gated radiotherapy. J. Appl. Clin. Med. Phys. 9(2):57–68, 2008.
Dieterich, S. et al., Respiratory skin motion tracking in stereotactic radiosurgery with the CyberKnife (WIP). Med.Phys. 1256(1256):130–136, 2003.
Linthout, N. et al., Six dimensional analysis with daily stereoscopic x-ray imaging of intrafraction patient motion in head and neck treatments using five points fixation masks. Med. Phys. 33(2):504–513, 2006.
Liere, R. V., and Mulder, J. D., Optical Tracking Using Projective Invariant Marker Pattern Properties. In: IEEE Virtual Reality, pp. 191–198, Los Angeles, 2003.
Steinicke, F., et al “Generating optimized marker-based rigid bodies for optical tracking systems”, in Second International Conference on Computer Vision Theory & Applications, pp. 387–395, Barcelona, Spain, 2007
Ribo, M., Pinz, A., Fuhrmann, A. L. (2001) “A new optical tracking system for virtual and augmented reality applications,” the 18th IEEE conference on Instrumentation and Measurement Technology, pp. 1932–1936, IEEE, Budapest, Hungary.
Yan, G., Li, J., Huan, Y. et al (2014) “Ghost marker detection and elimination in marker-based optical tracking systems for real-time tracking in stereotactic body radiotherapy,” Med. Phys. 41(10): 101713–1–101713-10.
Lin, Q. et al., Robust stereo-match algorithm for infrared markers in image-guided optical tracking system. IEEE Access 6:52421–52433, 2018.
Ioannou, D. et al., Circle recognition through a 2D hough transform and radius histogramming. Image Vis. Comput. 17(1):15–26, 1999.
Lestriandoko, N. H., and Sadikin, R., (2017) “Circle detection based on hough transform and Mexican Hat filter”, International Conference on Computer, pp. 153–157, IEEE, Tangerang, Indonesia.
Zhang, M., and Cao, H., “A new method of circle’s center and radius detection in image processing”, IEEE International Conference on Automation & Logistics, pp. 2239–2242, IEEE, Qingdao, Peoples Republic of China 2008
Cai, K. et al., Near-Infrared camera calibration for optical surgical navigation. J. Med. Syst. 40(3):1–12, 2016.
Canny, J., A computational approach to edge detection. IEEE T Pattern Anal. 8(6):679–698, 1986.
Suzuki, S., and Be, K., Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing. 30(1):32–46, 1985.
Fitzgibbon, A. W., et al “Direct least squares fitting of ellipses”, International Conference on Pattern Recognition. pp. 253–257, IEEE Comput. Soc. Press, Vienna, Austria, 1996
Funding
This study was supported by the National Natural Scientific Foundation of China (81671788), the Guangdong Provincial Science and Technology Program (2016A020220006, 2017B020210008, and 2017B010110015), the China Postdoctoral Science Foundation (2017 M612671), the Fundamental Research Funds for Central Universities (2017ZD082, x2yxD2182720), the Guangzhou Science and Technology Program (201704020228), and the Chinese Scholarship Fund (201806155010).
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Wu, H., Lin, Q., Yang, R. et al. An Accurate Recognition of Infrared Retro-Reflective Markers in Surgical Navigation. J Med Syst 43, 153 (2019). https://doi.org/10.1007/s10916-019-1257-x
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DOI: https://doi.org/10.1007/s10916-019-1257-x