Fast and Accurate Bronchoscope Tracking Using Image Registration and Motion Prediction

  • Jiro Nagao
  • Kensaku Mori
  • Tsutomu Enjouji
  • Daisuke Deguchi
  • Takayuki Kitasaka
  • Yasuhito Suenaga
  • Jun-ichi Hasegawa
  • Jun-ichiro Toriwaki
  • Hirotsugu Takabatake
  • Hiroshi Natori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3217)

Abstract

This paper describes a method for faster and more accurate bronchoscope camera tracking by image registration and camera motion prediction using the Kalman filter. The position and orientation of the bronchoscope camera at a frame of a bronchoscopic video are predicted by the Kalman filter. Because the Kalman filter gives good prediction for image registration, estimation of the position and orientation of the bronchoscope tip converges fast and accurately. In spite of the usefulness of Kalman filters, there have been no reports on tracking bronchoscope camera motion using the Kalman filter. Experiments on eight pairs of real bronchoscopic video and chest CT images showed that the proposed method could track camera motion 2.5 times as fast as our previous method. Experimental results showed that the motion prediction increased the number of frames correctly and continuously tracked by about 4.5%, and the processing time was reduced by about 60% with the search space restriction also proposed in this paper.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mori, K., Suenaga, Y., Toriwaki, J., et al.: Tracking of camera motion of real endoscope by using the Virtual Endoscope System. In: Lemke, H.U., Vannier, M.W., Inamura, K., et al. (eds.) CARS 2000. International Congress Series, vol. 1214, pp. 85–90 (2000)Google Scholar
  2. 2.
    Deguchi, D., Mori, K., Suenaga, Y., et al.: New Image Similarity Measure for Bronchoscope Tracking Based on Image Registration. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 399–406. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Vining, D.J., Shitrin, R.Y., Haponik, E.F., et al.: Virtual Bronchoscopy, Radiology, 193 (P), Supplement to Radiology RSNA Scientific Program, p.261 (1994) Google Scholar
  4. 4.
    Rogalla, P., Terwisscha van Scheltinga, J., Hamm, B. (eds.): Virtual endoscopy and related 3D techniques. Springer, Berlin (2001)Google Scholar
  5. 5.
    Forsyth, D.A., Ponce, J.: Computer Vision A Modern Approach. Pearson Education, London (2003)Google Scholar
  6. 6.
    Bricault, G.F., Cinquin, P.: Registration Real and CT-Derived Virtual Bronchoscopic Images to Assist Transbronchial Biopsy. IEEE Trans. on Medical Imaging 17(5), 703–714 (1998)CrossRefGoogle Scholar
  7. 7.
    Helferty, J.P., Higgins, W.E.: Technique for Registering 3D Virtual CT Images to Endoscopic Video. In: Proceedings of ICIP, pp. 893–896 (2001)Google Scholar
  8. 8.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., et al.: Numerical Recipes in C, The Art of Scientific Computing, 2nd edn., pp. 321–336. Cambridge University Press, Cambridge (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jiro Nagao
    • 1
  • Kensaku Mori
    • 1
  • Tsutomu Enjouji
    • 1
  • Daisuke Deguchi
    • 1
  • Takayuki Kitasaka
    • 1
  • Yasuhito Suenaga
    • 1
  • Jun-ichi Hasegawa
    • 2
  • Jun-ichiro Toriwaki
    • 2
  • Hirotsugu Takabatake
    • 3
  • Hiroshi Natori
    • 4
  1. 1.Graduate School of Information ScienceNagoya UniversityJapan
  2. 2.School of Computer and Cognitive SciencesChukyo UniversityJapan
  3. 3.Sapporo Minami-sanjyo HospitalSapporoJapan
  4. 4.Department of Diagnostic Ultrasound and Medical ElectronicsSapporo Medical UniversityJapan

Personalised recommendations