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.
Keywords
- Optical Coherence Tomography
- Root Mean Square Deviation
- Markov Random Fields
- Manual Segmentation
- Active Contour Model
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References
Ballard, D.H.: Generalizing the hough transform to detect arbitraty shapes. Pattern Recognition 13(2), 111–122 (1981)
Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, B 36, 192–236 (1974)
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)
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)
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)
Hori, Y., Yasuno, Y.: Automatic Characterization and Segmentation of Human Skin using Three-Dimensional Optical Coherence Tomography. Optics Express 14(5), 1862–1877 (2006)
Illingworth, J., Kittler, J.: A Survey of the Hough Transform. Computer Vision, Graphics, and Image Processing 44, 87–116 (1988)
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)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)
Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3 edn. Springer Publishing Company, Incorporated (2009)
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)
Viterbi, A.J.: Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Trans. on Inform. Theory 13(2), 260–269 (1967)
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)
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)
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Müller, O. et al. (2011). Model Based 3D Segmentation and OCT Image Undistortion of Percutaneous Implants. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_56
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DOI: https://doi.org/10.1007/978-3-642-23626-6_56
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