Advertisement

An Illumination Model of the Trachea Appearance in Videobronchoscopy Images

  • Carles Sánchez
  • Javier Sánchez
  • Antoni Rosell
  • Debora Gil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways. This imaging modality provides realistic images and allows non-invasive minimal intervention procedures. Tracheal procedures are routinary interventions that require assessment of the percentage of obstructed pathway for injury (stenosis) detection. Visual assessment in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error.

This paper introduces an automatic method for the estimation of steneosed trachea percentage reduction in videobronchoscopic images. We look for tracheal rings , whose deformation determines the degree of obstruction. For ring extraction , we present a ring detector based on an illumination and appearance model. This model allows us to parametrise the ring detection. Finally, we can infer optimal estimation parameters for any video resolution.

Keywords

Bronchoscopy tracheal ring stenosis assesment trachea appearance model segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bolliger, C.T., Mathur, P.N.: Interventional bronchoscopy, vol.30. S. Karger Ag (2000)Google Scholar
  2. 2.
    Norwood, S., Vallina, V.L., Short, K., Saigusa, M., Fernandez, L.G., McLarty, J.W.: Incidence of tracheal stenosis and other late complications after percutaneous tracheostomy. Annals of surgery 232(2), 233 (2000)CrossRefGoogle Scholar
  3. 3.
    Vergnon, J.M., Costes, F., Bayon, M.C., Emonot, A.: Efficacy of tracheal and bronchial stent placement on respiratory functional tests. Chest 107(3), 741–746 (1995)CrossRefGoogle Scholar
  4. 4.
    Deguchi, D., Mori, K., Feuerstein, M., Kitasaka, T., Maurer, C.R., Suenaga, Y., Takabatake, H., Mori, M., Natori, H.: Selective image similarity measure for bronchoscope tracking based on image registration. Medical Image Analysis 13(4), 621–633 (2009)CrossRefGoogle Scholar
  5. 5.
    Luo, X., Kitasaka, T., Mori, K.: ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 248–255. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Reichl, T., Luo, X., Menzel, M., Hautmann, H., Mori, K., Navab, N.: Deformable Registration of Bronchoscopic Video Sequences to CT Volumes with Guaranteed Smooth Output. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 17–24. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Luo, X., Kitasaka, T., Mori, K.: Bronchoscopy Navigation beyond Electromagnetic Tracking Systems: A Novel Bronchoscope Tracking Prototype. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 194–202. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Schwarz, Y., Greif, J., Becker, H.D., Ernst, A., Mehta, A.: Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid ct images. Chest 129(4), 988–994 (2006)CrossRefGoogle Scholar
  9. 9.
    Freeman, W.T., Adelson, E.H.: Massachusetts Institute of Technology. Media Laboratory. Vision, and Modeling Group, “The design and use of steerable filters”. IEEE Transactions on Pattern analysis and machine intelligence 13(9), 891–906 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carles Sánchez
    • 1
  • Javier Sánchez
    • 1
  • Antoni Rosell
    • 2
  • Debora Gil
    • 1
  1. 1.Comp. Vision Center, Comp. Science Dep.UABSpain
  2. 2.IDIBELL, CIBERESPneumology Unit, Hosp. Univ. BellvitgeSpain

Personalised recommendations