3D Tracking of Laparoscopic Instruments Using Statistical and Geometric Modeling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)


During a laparoscopic surgery, the endoscope can be manipulated by an assistant or a robot. Several teams have worked on the tracking of surgical instruments, based on methods ranging from the development of specific devices to image processing methods. We propose to exploit the instruments’ insertion points, which are fixed on the patients abdominal cavity, as a geometric constraint for the localization of the instruments. A simple geometric model of a laparoscopic instrument is described, as well as a parametrization that exploits a spherical geometric grid, which offers attracting homogeneity and isotropy properties. The general architecture of our proposed approach is based on the probabilistic Condensation algorithm.


laparoscopic surgery image-based localization of surgical instruments Condensation algorithm 


  1. 1.
    Aggarwal, R., Grantcharov, T., Moorthy, K., Milland, T., Darzi, A.: Toward feasible, valid, and reliable video-based assessments of technical surgical skills in the operating room. Annals of Surgery 247(2), 372 (2008)CrossRefGoogle Scholar
  2. 2.
    Cano, A.M., Gayá, F., Lamata, P., Sánchez-González, P., Gómez, E.J.: Laparoscopic tool tracking method for augmented reality surgical applications. In: Bello, F., Edwards, E. (eds.) ISBMS 2008. LNCS, vol. 5104, pp. 191–196. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Doignon, C., Graebling, P., de Mathelin, M.: Real-time segmentation of surgical instruments inside the abdominal cavity using a joint hue saturation color feature. Real-Time Imaging 11(5-6), 429–442 (2005)CrossRefGoogle Scholar
  4. 4.
    Isard, M., Blake, A.: Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  5. 5.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Randall, D.A., Ringler, T.D., Heikes, R.P., Jones, P., Baumgardner, J.: Climate modeling with spherical geodesic grids. Computing in Science and Engineering 4, 32–41 (2002)CrossRefGoogle Scholar
  7. 7.
    Sherman, V., Feldman, L.S., Stanbridge, D., Kazmi, R., Fried, G.M.: Assessing the learning curve for the acquisition of laparoscopic skills on a virtual reality simulator. Surgical Endoscopy 19(5), 678–682 (2005)CrossRefGoogle Scholar
  8. 8.
    Touijer, K., Guillonneau, B.: Laparoscopic radical prostatectomy: A critical analysis of surgical quality. European Urology 49(4), 625 (2006)CrossRefGoogle Scholar
  9. 9.
    Uecker, D.R., Lee, C., Wang, Y.F., Wang, Y.: Automated instrument tracking in robotically assisted laparoscopic surgery. Journal of Image Guided Surgery 1(6), 308–325 (1995)CrossRefGoogle Scholar
  10. 10.
    Voros, S., Long, J.A., Cinquin, P.: Automatic detection of instruments in laparoscopic images: A first step towards high-level command of robotic endoscopic holders. International Journal of Robotics Research 26(11-12), 1173–1190 (2007)CrossRefGoogle Scholar
  11. 11.
    Zhang, Z.Y.: A flexible new technique for camera calibration. IEEE Transactions On Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.UJF-Grenoble 1, CNRSFrance
  2. 2.UJF-Grenoble 1, CNRS, INSERM TIMC-IMAG UMR 5525GrenobleFrance

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