Abstract
Current Minimally Invasive Surgery (MIS) technology, although advantageous compared to open cavity surgery in many aspects, has limitations that prevents its use for general purpose surgery. This is due to reduced dexterity, cost, and required complex training of the currently practiced technology. The main challenges in reducing cost and amount of training is to have an accurate inner body navigation advisory system to help guide the surgeon to reach the surgery location. As a first step in making minimally invasive surgery affordable and more user friendly, quality images inside the patient as well as the surgical tool location should be provided automatically and accurately in real time in a common reference frame. The objective of this paper is to build a platform to accomplish this goal. It is shown that a set of three heterogeneous asynchronous sensors is a minimum requirement for navigation inside the human body. The sensors have different data rate, different reference frames, and independent time clocks. A prerequisite for successful information fusion is to represent all the sensors data in a common reference frame. The focus of this paper is on off-line calibration of the three sensors, i.e. before the surgical device is inserted in the human body. This is a pre-requisite for real time navigation inside the human body. The proposed off-line sensor registration technique was tested using experimental laboratory data. The result of calibration was promising with an average error of 0.1081 mm and 0.0872 mm along the x and y directions, respectively, in the 2D camera image.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Thoranaghatte, R.U., et al.: Endoscope-based hybrid navigation system for minimally invasive ventral spine surgeries. Comput. Aided Surg. 10(5–6), 351–356 (2005)
Peters, T., Cleary, K.: Image-Guided Interventions: Technology and Applications. Springer Science and Business Media (2008)
Nakada, K. et al.: A rapid method for magnetic tracker calibration using a magneto-optic hybrid tracker, In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 285–293 (2003)
Feuerstein, M., et al.: Magneto-optical tracking of flexible laparoscopic ultrasound: model-based online detection and correction of magnetic tracking errors. IEEE Trans. Med. Imaging 28(6), 951–967 (2009)
Nakamoto, M., et al.: Intraoperative magnetic tracker calibration using a magneto-optic hybrid tracker for 3-D ultrasound-based navigation in laparoscopic surgery. IEEE Trans. Med. Imaging 27(2), 255–270 (2008)
Fakhfakh, H.E., et al.: Automatic registration of pre-and intraoperative data for long bones in minimally invasive surgery, In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5575–5578 (2014)
Martens, V., et al.: LapAssistent—a laparoscopic liver surgery assistance system. In: 4th European Conference of the International Federation for Medical and Biological Engineering, pp. 121–125 (2009)
Wengert, C., et al.: Endoscopic navigation for minimally invasive suturing. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 620–627 (2007)
Thompson, S., et al.: Hand–eye calibration for rigid laparoscopes using an invariant point. Int. J. Comput. Assist. Radiol. Surg. 11(6), 1071–1080 (2016)
Solberg, O.V., et al.: Navigated ultrasound in laparoscopic surgery. Minim. Invasive Ther. Allied Technol. 18(1), 36–53 (2009)
Konishi, K., et al.: A real-time navigation system for laparoscopic surgery based on three-dimensional ultrasound using magneto-optic hybrid tracking configuration. Int. J. Comput. Assist. Radiol. Surg. 2(1), 1–10 (2007)
Birkfellner, W., et al.: Calibration of tracking systems in a surgical environment. IEEE Trans. Med. Imaging 17(5), 737–742 (1998)
Nakamoto, M., et al.: Magneto-optic hybrid 3-D sensor for surgical navigation. In: MICCAI, pp. 839–848 (2000)
Prager, R.W., et al.: Rapid calibration for 3-D freehand ultrasound. Ultrasound Med. Biol. 24(6), 855–869 (1998)
Sato, Y., et al.: Image guidance of breast cancer surgery using 3-D ultrasound images and augmented reality visualization. IEEE Trans. Med. Imaging 17(5), 681–693 (1998)
Chaoui, J., et al.: Virtual movements-based calibration method of ultrasound probe for computer assisted surgery. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (2009)
Transtrum, M.K., Sethna, J. P.: Improvements to the Levenberg-Marquardt algorithm for nonlinear least-squares minimization. arXiv Preprint arXiv:1201.5885 (2012)
Pyciński, B., et al.: Image navigation in minimally invasive surgery. Inf. Technol. Biomed. 4, 25–34 (2014)
Jose Estepar, R.S., et al.: Towards scarless surgery: an endoscopic ultrasound navigation system for transgastric access procedures. Comput. Aided Surg. 12(6), 311–324 (2007)
Horn, B.K.: Closed-form solution of absolute orientation using unit quaternions. Josa A 4(4), 629–642 (1987)
Cash, D.M., et al.: Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking. Med. Phys. 30(7), 1671–1682 (2003)
Birkfellner, W., et al.: Tracking devices. In: Image-Guided Interventions. Springer (2008)
Marami, B., et al.: Dynamic tracking of a deformable tissue based on 3D-2D MR-US image registration, In: SPIE Medical Imaging, International Society for Optics and Photonics, pp. 90360T (2014)
Sindram, D., et al.: Novel 3-D laparoscopic magnetic ultrasound image guidance for lesion targeting. Hpb 12(10), 709–716 (2010)
Mercier, L., et al.: A review of calibration techniques for freehand 3-D ultrasound systems. Ultrasound Med. Biol. 31(2), 143–165 (2005)
Ng, C.S., et al.: Hybrid DynaCT-guided electromagnetic navigational bronchoscopic biopsy. Eur. J. Cardiothoracic Surg. 49(suppl_1), i88 (2015)
Franz, A.M., et al.: Electromagnetic tracking in medicine—a review of technology, validation, and applications. IEEE Trans. Med. Imaging 33(8), 1702–1725 (2014)
NextEngine 3D Laser Scanner. http://www.nextengine.com/products/scanner/specs
GO-5000 M-USB/ GO-5000C-USB. https://www.jai.com/products/go-5000c-usb
Sauer, F.: Image registration: Enabling technology for image guided surgery and therapy, In: 27th Annual International Conference of the 2005 Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 7242–7245 (2006)
Ruehling, D.E.: Development and Testing of a Hybrid Medical Tracking System for Surgical Use. MS Thesis, Tennessee Technological University, Cookeville, TN (2015)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision (2003)
Gonzalez, R.C., Woods, R.E.: Digital image processing (2012)
Camera Calibration Toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/index.html
Walker, M.W., Shao, L., Volz, R.A.: Estimating 3-D location parameters using dual number quaternions. CVGIP: Image Understanding 54(3), 358–367 (1991)
Feng, H., Liu, Y., Xi, F.: Analysis of digitizing errors of a laser scanning system. Precis. Eng. 25(3), 185–191 (2001)
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
Bhattarai, U., Alouani, A.T. (2020). Hybrid Navigation Information System for Minimally Invasive Surgery: Offline Sensors Registration. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-17798-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17797-3
Online ISBN: 978-3-030-17798-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)