Model Based 3D Segmentation and OCT Image Undistortion of Percutaneous Implants

  • Oliver Müller
  • Sabine Donner
  • Tobias Klinder
  • Ralf Dragon
  • Ivonne Bartsch
  • Frank Witte
  • Alexander Krüger
  • Alexander Heisterkamp
  • Bodo Rosenhahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Optical Coherence Tomography (OCT) is a noninvasive im-aging technique which is used here for in vivo biocompatibility studies of percutaneous implants. A prerequisite for a morphometric analysis of the OCT images is the correction of optical distortions caused by the index of refraction in the tissue. We propose a fully automatic approach for 3D segmentation of percutaneous implants using Markov random fields. Refraction correction is done by using the subcutaneous implant base as a prior for model based estimation of the refractive index using a generalized Hough transform. Experiments show the competitiveness of our algorithm towards manual segmentations done by experts.

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References

  1. 1.
    Ballard, D.H.: Generalizing the hough transform to detect arbitraty shapes. Pattern Recognition 13(2), 111–122 (1981)CrossRefMATHGoogle Scholar
  2. 2.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, B 36, 192–236 (1974)MathSciNetMATHGoogle Scholar
  3. 3.
    Eichel, J.A., Bizheva, K.K., Clausi, D.A., Fieguth, P.W.: Automated 3D Reconstruction and Segmentation from Optical Coherence Tomography. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 44–57. Springer, Heidelberg (2010)Google Scholar
  4. 4.
    Fernández, D.C., Salinas, H.M., Puliafito, C.A.: Automated detection of retinal layer structures on optical coherence tomography images. Optics Express 13(25), 10200–10216 (2005)CrossRefGoogle Scholar
  5. 5.
    Garvin, M.K., Abrámoff, M.D., Kardon, R., Russell, S.R., Wu, X., Sonka, M.: Intraretinal Layer Segmentation of Macular Optical Coherenec Tomography Images Using Optimal 3-D Graph Search. IEEE Transactions on Medical Imaging 27(10), 1495–1505 (2008)CrossRefGoogle Scholar
  6. 6.
    Hori, Y., Yasuno, Y.: Automatic Characterization and Segmentation of Human Skin using Three-Dimensional Optical Coherence Tomography. Optics Express 14(5), 1862–1877 (2006)CrossRefGoogle Scholar
  7. 7.
    Illingworth, J., Kittler, J.: A Survey of the Hough Transform. Computer Vision, Graphics, and Image Processing 44, 87–116 (1988)CrossRefGoogle Scholar
  8. 8.
    Karimaghaloo, Z., Shah, M., Francis, S.J., Arnold, D.L., Collins, D.L., Arbel, T.: Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 41–48. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefMATHGoogle Scholar
  10. 10.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3 edn. Springer Publishing Company, Incorporated (2009)Google Scholar
  11. 11.
    Tearney, G.J., Brezinski, M.E., Southern, J.F., Bouma, B.E., Hee, M.R., Fujimoto, J.G.: Determination of the refractive index of highly scattering human tissue by optical coherence tomography. Optics Letters 20(21), 2258–2260 (1995)CrossRefGoogle Scholar
  12. 12.
    Viterbi, A.J.: Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Trans. on Inform. Theory 13(2), 260–269 (1967)CrossRefMATHGoogle Scholar
  13. 13.
    Westphal, V., Rollins, A.M., Radhakrishnan, S.: Correction of geometric and refractive image distortions in optical coherence tomography applying Fermats principle. Optics Express 10(9), 397–404 (2002)CrossRefGoogle Scholar
  14. 14.
    Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic, M.: Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 649–656. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oliver Müller
    • 1
  • Sabine Donner
    • 2
    • 3
  • Tobias Klinder
    • 4
  • Ralf Dragon
    • 1
  • Ivonne Bartsch
    • 3
  • Frank Witte
    • 3
  • Alexander Krüger
    • 2
    • 3
  • Alexander Heisterkamp
    • 2
    • 3
  • Bodo Rosenhahn
    • 1
  1. 1.Institut für InformationsverarbeitungLeibniz Universität HannoverHannoverGermany
  2. 2.Laser Zentrum Hannover e.V.HannoverGermany
  3. 3.CrossBIT, Center for Biocompatibility and Implant-ImmunologyHannover Medical SchoolHannoverGermany
  4. 4.Philips Research North AmericaBriarcliff ManorUSA

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