Advertisement

Continuous Zoom Calibration by Tracking Salient Points in Endoscopic Video

  • Miguel Lourenço
  • João P. Barreto
  • Fernando Fonseca
  • Hélder Ferreira
  • Rui M. Duarte
  • Jorge Correia-Pinto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Many image-based systems for aiding the surgeon during minimally invasive surgery require the endoscopic camera to be calibrated at all times. This article proposes a method for accomplishing this goal whenever the camera has optical zoom and the focal length changes during the procedure. Our solution for online calibration builds on recent developments in tracking salient points using differential image alignment, is well suited for continuous operation, and makes no assumptions about the camera motion or scene rigidity. Experimental validation using both a phantom model and in vivo data shows that the method enables accurate estimation of focal length when the zoom varies, avoiding the need to explicitly recalibrate during surgery. To the best of our knowledge this the first work proposing a practical solution for online zoom calibration in the operation room.

Keywords

Focal Length Camera Motion Camera Calibration Boundary Contour Distortion Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Melo, R., Barreto, J., Falcao, G.: A New Solution for Camera Calibration and Real-Time Image Distortion Correction in Medical Endoscopy-Initial Technical Evaluation. IEEE Transactions on Biomedical Engineering 59, 634–644 (2012)CrossRefGoogle Scholar
  2. 2.
    Burschka, D., Li, M., Taylor, R., Hager, G.: Scale-Invariant Registratiou of Monocular Endoscopic Images to CT-Scans for Sinus Surgery. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 413–421. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Yamaguchi, T., et al.: Camera Model and Calibration Procedure for Oblique-Viewing Endoscope. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 373–381. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Stoyanov, D., Darzi, A., Yang, G.Z.: Laparoscope Self-calibration for Robotic Assisted Minimally Invasive Surgery. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 114–121. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Stewenius, H., Nister, D., Kahl, F., Schaffalitzky, F.: A minimal solution for relative pose with unknown focal length. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 789–794 (2005)Google Scholar
  6. 6.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518Google Scholar
  7. 7.
    Lee, T.Y., et al.: Automatic distortion correction of endoscopic images captured with wide-angle zoom lens. IEEE Transactions on Biomedical Engineering 60, 2603–2613 (2013)CrossRefGoogle Scholar
  8. 8.
    Lourenço, M., Barreto, J.P.: Tracking Feature Points in Uncalibrated Images with Radial Distortion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 1–14. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Fitzgibbon, A.: Simultaneous linear estimation of multiple view geometry and lens distortion. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 125–132 (2001)Google Scholar
  10. 10.
    Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)CrossRefGoogle Scholar
  11. 11.
    Nister, D.: An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 756–770 (2004)CrossRefGoogle Scholar
  12. 12.
    Lourenco, M., Barreto, J., Vasconcelos, F.: sRD-SIFT: Keypoint Detection and Matching in Images With Radial Distortion. IEEE Transactions on Robotics 28, 752–760 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Miguel Lourenço
    • 1
  • João P. Barreto
    • 1
    • 2
  • Fernando Fonseca
    • 3
  • Hélder Ferreira
    • 4
  • Rui M. Duarte
    • 4
  • Jorge Correia-Pinto
    • 4
  1. 1.Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal
  2. 2.Perceive 3DCoimbraPortugal
  3. 3.Faculty of MedicineCoimbra Hospital and Universitary CentreCoimbraPortugal
  4. 4.Life and Health Sciences Research InstituteUniversity of MinhoBragaPortugal

Personalised recommendations