Belief Propagation for Depth Cue Fusion in Minimally Invasive Surgery

  • Benny Lo
  • Marco Visentini Scarzanella
  • Danail Stoyanov
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation recovery has motivated research into fusing multiple visual cues for depth recovery. In this paper, a Markov Random Field (MRF) based Bayesian belief propagation framework has been proposed for the fusion of different depth cues. By using the underlying MRF structure to ensure spatial continuity in an image, the proposed method offers the possibility of inferring surface depth by fusing the posterior node probabilities in a node’s Markov blanket together with the monocular and stereo depth maps. Detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique.

Keywords

Robotic Assisted Surgery Image Guided Intervention Intra-operative Navigation Markov Random Fields Belief Networks 

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References

  1. 1.
    Delingette, H., et al.: Computational Models for Image-Guided Robot-Assisted and Simulated Medical Interventions. Proceedings of the IEEE 94(9), 1678–1688 (2006)CrossRefGoogle Scholar
  2. 2.
    Pankanti, S., Jain, A.K.: Integrating vision modules: stereo, shading, grouping, and line labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(9), 831–842 (1995)CrossRefGoogle Scholar
  3. 3.
    Bozma, H.I., Duncan, J.S.: Integration of vision modules: a game-theoretic framework. In: Proc. CVPR (1991)Google Scholar
  4. 4.
    Shah, J., Pien, H.H., Gauch, J.M.: Recovery of surfaces with discontinuities by fusing shading and range data within a variational framework. IEEE Transactions on Image Processing 5(8), 1243–1251 (1996)CrossRefGoogle Scholar
  5. 5.
    Deguchi, K., Okatani, T.: Shape reconstruction from an endoscope image by shape-from-shading technique for a point light source at the projection center. In: Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis (1996)Google Scholar
  6. 6.
    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
  7. 7.
    Saxena, A., Schulte, J., Ng, A.: Depth Estimation using Monocular and Stereo Cues. Proc. IJCAI (2007)Google Scholar
  8. 8.
    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
  9. 9.
    Shi, J., Tomasi, C.: Good Features to Track, Cornell University (1993)Google Scholar
  10. 10.
    Matas, J., et al.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)CrossRefGoogle Scholar
  11. 11.
    Pilu, M.: A direct method for stereo correspondence based on singular value decomposition. In: Proc. CVPR. IEEE Computer Society, Los Alamitos (1997)Google Scholar
  12. 12.
    Ikeuchi, K., Horn, B.K.P.: Numerical shape from shading and occluding boundaries. In: Shape from shading, pp. 245–299. MIT Press, Cambridge (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Benny Lo
    • 1
  • Marco Visentini Scarzanella
    • 2
  • Danail Stoyanov
    • 1
  • Guang-Zhong Yang
    • 1
    • 2
  1. 1.Institute of Biomedical EngineeringUK
  2. 2.Royal Society/Wolfson Foundation MIC LaboratoryImperial College LondonUnited Kingdom

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