This study provides a framework for a single-camera odometry system for localizing a surgical C-arm base. An application-specific monocular visual odometry system (a downward-looking consumer-grade camera rigidly attached to the C-arm base) is proposed in this research. The cumulative dead-reckoning estimation of the base is extracted based on frame-to-frame homography estimation. Optical-flow results are utilized to feed the odometry. Online positional and orientation parameters are then reported. Positional accuracy of better than 2% (of the total traveled distance) for most of the cases and 4% for all the cases studied and angular accuracy of better than 2% (of absolute cumulative changes in orientation) were achieved with this method. This study provides a robust and accurate tracking framework that not only can be integrated with the current C-arm joint-tracking system (i.e. TC-arm) but also is capable of being employed for similar applications in other fields (e.g. robotics).
AmiriSWilsonDRMasriBAet al. A novel multi-planar radiography method for three dimensional pose reconstruction of the patellofemoral and tibiofemoral joints after arthroplasty. J Biomech2011; 44: 1757–1764.
2.
AmiriSWilsonDRMasriBAet al. A low-cost tracked C-arm (TC-arm) upgrade system for versatile quantitative intraoperative imaging. Int J Comput Assist Radiol Surg2013; 9: 695–711.
3.
ReaungamornratSOtakeYUneriAet al. Tracker-on-C for cone-beam CT-guided surgery: evaluation of geometric accuracy and clinical applications. In: Proceedings of the SPIE 8316 medical imaging conference: image-guided procedures, robotic interventions and modeling, San Diego, CA, 23 February 2012. Bellingham, WA: SPIE.
4.
GrzedaVFichtingerG (Inventor and Assignee). C-arm rotation encoding methods and apparatus. Patent US9008279 B2, 22June2010.
5.
WolffTLassoAEblenkampMet al. C-arm angle measurement with accelerometer for brachytherapy: an accuracy study. Int J Comput Assist Radiol Surg2014; 9: 137–144.
6.
KneipLChliMSiegwartR.Robust real-time visual odometry with a single camera and an IMU. In: Proceedings of the British machine vision conference (ed HoeyJMcKennaSTruccoE), Dundee, 29 August–2 September 2011, pp.16.1–16.11. Guildford: BMVA Press.
7.
DavisonAJReidIDMoltonNDet al. MonoSLAM: real-time single camera SLAM. IEEE T Pattern Anal2007; 29: 1052–1067.
8.
ChliM.Applying information theory to efficient SLAM. PhD Thesis, Imperial College London, London, 2010.
9.
ScaramuzzaDFraundorferFSiegwartR. Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC. In: Proceedings of the IEEE international conference on robotics and automation (ICRA ‘09), Kobe, Japan, 12–17 May 2009, pp.4293–4299. New York: IEEE.
10.
HornBKPSchunckBG. “Determining optical flow”: a retrospective. Artif Intell1993; 59: 81–87.
11.
LucasBDKanadeT.An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence (IJCAI ‘81), Vancouver, BC, Canada, 24–28 August 1981, pp.674–679. San Francisco, CA: Morgan Kaufmann Publishers.
12.
DericheRKornprobstPAubertG.Optical-flow estimation while preserving its discontinuities: a variational approach. In: Proceedings of the 2nd Asian conference on computer vision (ACCV ‘95), Singapore, 5–8 December 1995, pp. 69–80. Berlin: Springer.
13.
AlvarezLWeickertJSanchezJ.Reliable estimation of dense optical flow fields with large displacements. Int J Comput Vision2000; 39: 41–56.
14.
AnandanP.A computational framework and an algorithm for the measurement of visual motion. Int J Comput Vision1989; 2: 283–310.
15.
CampbellJSukthankarRNourbakhshIet al. A robust visual odometry and precipice detection system using consumer-grade monocular vision. In: Proceedings of the international conference on robotics and automation, Barcelona, 18–22 April 2005, pp.3421–3427. New York: IEEE.
16.
ForsterCPizzoliMScaramuzzaDet al. SVO: fast semi-direct monocular visual odometry. In: Proceedings of the IEEE international conference on robotics and automation, Honk Kong, China, 31 May–7 June 2014, pp.15–22. New York: IEEE.
17.
SilvaBBurlamaquiAGoncalvesL. On monocular visual odometry for indoor ground vehicles. In: Proceedings of the Brazilian robotics symposium and Latin American robotics symposium, Fortaleza, Brazil, 16–19 October 2012, pp.220–225. New York: IEEE.
18.
HartleyRZissermanA.Multiple view geometry in computer vision. Cambridge: Cambridge University Press, 2003.
19.
ZhangZ.A flexible new technique for camera calibration. IEEE T Pattern Anal2000; 22: 1330–1334.
20.
ShapiroR.Direct linear transformation method for three-dimensional cinematography. Res Q1978; 49: 197–205.
21.
Abdel-AzizYIKararaHM.Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In: Proceedings of the American society of photogrammetry symposium on close-range photogrammetry, Washington, DC, 1971, pp.1–18. Falls Church, VA: ASP.
22.
BakerSMatthewsI.Lucas-Kanade 20 years on: a unifying framework. Int J Comput Vision2004; 56: 221–255.
23.
HarrisCStephensM.A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, Manchester, 31 August–2 September 1988, pp.147–151.
