Laparoscope Self-calibration for Robotic Assisted Minimally Invasive Surgery

  • Danail Stoyanov
  • Ara Darzi
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3750)

Abstract

For robotic assisted minimal access surgery, recovering 3D soft tissue deformation is important for intra-operative surgical guidance, motion compensation, and prescribing active constraints. We propose in this paper a method for determining varying focal lengths of stereo laparoscope cameras during robotic surgery. Laparoscopic images typically feature dynamic scenes of soft-tissue deformation and self-calibration is difficult with existing approaches due to the lack of rigid temporal constraints. The proposed method is based on the direct derivation of the focal lengths from the fundamental matrix of the stereo cameras with known extrinsic parameters. This solves a restricted self-calibration problem, and the introduction of the additional constraints improves the inherent accuracy of the algorithm. The practical value of the method is demonstrated with analysis of results from both synthetic and in vivo data sets.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Taylor, R.H., Stoianovici, D.: Medical Robotics in Computer-Integrated Surgery. IEEE Transactions on Robotics and Automation 19, 765–781 (2003)CrossRefGoogle Scholar
  2. 2.
    Stoyanov, D., Darzi, A., Yang, G.-.Z.: Dense 3D Depth Recovery for Soft Tissue Deformation During Robotically Assisted Laparoscopic Surgery. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 41–48. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Mourgues, F., Coste-Manière, È.: Flexible calibration of actuated stereoscopic endoscope for overlay in robot assisted surgery. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 24–34. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Pollefeys, M.: Self-Calibration and Metric 3D Reconstruction from Uncalibrated Image Sequences. PhD Thesis, Katholieke Universiteit Leuven, Belgium (1999)Google Scholar
  5. 5.
    Hartley, R.I.: Estimation of Relative Camera Positions for Uncalibrated Cameras. In: Proc. ECCV, pp. 579–587 (1992)Google Scholar
  6. 6.
    Bougnoux, S.: From Projective to Euclidean Space under Any Practical Situation, A Criticism of Self-Calibration. In: Proc. ICCV, pp. 790–796 (1998)Google Scholar
  7. 7.
    Kanatani, K., Matsunaga, C.: Closed Form Expressions for Focal Lengths from the Fundamental Matrix. In: Proc. ACCV, pp. 128–133 (2000)Google Scholar
  8. 8.
    Brooks, M.J., De Agapito, L., Huynh, D.Q., Baumela, L.: Towards Robust Metric Reconstruction Via A Dynamic Uncalibrated Stereo Head. Image and Vision Computing 16, 989–1002 (1998)CrossRefGoogle Scholar
  9. 9.
    De Agapito, L., Huynh, D.Q., Brooks, M.J.: Self Calibrating a Stereo Head: An Error Analysis in The Neighbourhood of Degenerate Configurations. In: Proc. ICCV, pp. 747–753 (1998)Google Scholar
  10. 10.
    Sturm, P.: On Focal Length Calibration from Two Views. In: Proc. CVPR, pp. 145–150 (2000)Google Scholar
  11. 11.
    Sturm, P.: Critical Motion Sequences for the Self-Calibration of Cameras and Stereo Systems with Variable Focal Length. Image and Vision Computing 20, 415–426 (2002)CrossRefGoogle Scholar
  12. 12.
    Frahm, F., Koch, R.: Camera Calibration with known Rotation. In: Proc. ICCV, pp. 1418–1425 (2003)Google Scholar
  13. 13.
    Stein, F.: Accurate Internal Camera Calibration Using Rotation with Analysis of Sources of Error. In: Proc. ICCV, pp. 230–236 (1995)Google Scholar
  14. 14.
    McLauchlan, P.F., Murray, D.W.: Active camera calibration for a head-eye platform using the variable state-dimension filter. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 15–22 (1996)CrossRefGoogle Scholar
  15. 15.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  16. 16.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)CrossRefGoogle Scholar
  17. 17.
    Pilu, M.: A Direct Method for Stereo Correspondence based on Singular Value Decomposition. In: Proc. CVPR, pp. 261–266 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Danail Stoyanov
    • 1
  • Ara Darzi
    • 2
  • Guang-Zhong Yang
    • 1
    • 2
  1. 1.Medical Image Computing LaboratoryRoyal Society/Wolfson FoundationUK
  2. 2.Department of Surgical Oncology and TechnologyImperial College of Science, Technology and MedicineLondonUK

Personalised recommendations