QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data

  • Franziska SchirrmacherEmail author
  • Thomas Köhler
  • Lennart Husvogt
  • James G. Fujimoto
  • Joachim Hornegger
  • Andreas K. Maier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.


Alternating Direction Method Of Multipliers (ADMM) Speckle Noise Preserve Image Structures Denoising Methods Efficient Energy Minimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cheng, J., Duan, L., Wong, D.W.K., Tao, D., Akiba, M., Liu, J.: Speckle reduction in optical coherence tomography by image registration and matrix completion. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 162–169. Springer, Cham (2014). doi: 10.1007/978-3-319-10404-1_21CrossRefGoogle Scholar
  2. 2.
    Choi, W., Potsaid, B., Jayaraman, V., Baumann, B., Grulkowski, I., Liu, J.J., Lu, C.D., Cable, A.E., Huang, D., Duker, J.S., Fujimoto, J.G.: Phase-sensitive swept-source optical coherence tomography imaging of the human retina with a vertical cavity surface-emitting laser light source. Opt. Lett. 38(3), 338 (2013)CrossRefGoogle Scholar
  3. 3.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 145–149 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Duan, J., Lu, W., Tench, C., Gottlob, I., Proudlock, F., Samani, N.N., Bai, L.: Denoising optical coherence tomography using second order total generalized variation decomposition. Biomed. Signal Process. Control 24, 120–127 (2016)CrossRefGoogle Scholar
  5. 5.
    Fang, L., Li, S., Nie, Q., Izatt, J.A., Toth, C.A., Farsiu, S.: Sparsity based denoising of spectral domain optical coherence tomography images. Biomed. Opt. Express 3(5), 927–942 (2012)CrossRefGoogle Scholar
  6. 6.
    Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Pan, J., Sun, D., Hanspeter, P., Yang, M.-H.: Blind image deblurring using dark channel prior. In: Proceedings of CVPR 2016, pp. 1628–1636 (2016)Google Scholar
  8. 8.
    Köhler, T., Bock, R., Hornegger, J., Michelson, G.: Computer-aided diagnostics and pattern recognition: automated glaucoma detection. In: Michelson, G. (ed.) Teleophthalmology in Preventive Medicine, pp. 93–104. Springer, Cham (2015)Google Scholar
  9. 9.
    Mayer, M.A., Borsdorf, A., Wagner, M., Hornegger, J., Mardin, C.Y., Tornow, R.P.: Wavelet denoising of multiframe optical coherence tomography data. Biomed. Opt. Express 3(3), 572 (2012)CrossRefGoogle Scholar
  10. 10.
    Ochs, P., Dosovitskiy, A., Brox, T., Pock, T.: On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision. SIAM J. Imaging Sci. 8(1), 331–372 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ozcan, A., Bilenca, A., Desjardins, A.E., Bouma, B.E., Tearney, G.J.: Speckle reduction in optical coherence tomography images using digital filtering. J. Opt. Soc. Am. A 24(7), 1901 (2007)CrossRefGoogle Scholar
  12. 12.
    Pircher, M., Gotzinger, E., Leitgeb, R., Fercher, A.F., Hitzenberger, C.K.: Speckle reduction in optical coherence tomography by frequency compounding. J. Biomed. Opt. 8(3), 565 (2003)CrossRefGoogle Scholar
  13. 13.
    Romano, Y., Elad, M., Milanfar, P.: The Little Engine that Could: Regularization by Denoising (RED) (2016). arXiv preprint arXiv:1611.02862
  14. 14.
    Salinas, H., Fernandez, D.: Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imaging 26(6), 761–771 (2007)CrossRefGoogle Scholar
  15. 15.
    Wong, A., Mishra, A., Bizheva, K., Clausi, D.A.: General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery. Opt. Express 18(8), 8338–8352 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Franziska Schirrmacher
    • 1
    Email author
  • Thomas Köhler
    • 1
  • Lennart Husvogt
    • 1
  • James G. Fujimoto
    • 2
  • Joachim Hornegger
    • 1
  • Andreas K. Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Department of Electrical Engineering and Computer Science and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations