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Speckle Noise Reduction and Enhancement for OCT Images

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Retinal Optical Coherence Tomography Image Analysis

Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

The OCT imaging produces speckle noise due to multiple forward and backward scattered waves. The most important step of OCT preprocessing is noise reduction, which is helpful for more sophisticated processes like segmentation. Three OCT despeckling and enhancement methods are presented in this chapter, which are based on statistical modeling, data adaptive and non data adaptive transform based models, respectively.

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References

  1. D. Huang, E.A. Swanson, C.P. Lin, J.S. Schuman, W.G. Stinson, W. Chang et al., Optical coherence tomography. Science 254, 1178 (1991). (New York, NY)

    Article  ADS  Google Scholar 

  2. B. Potsaid, I. Gorczynska, V.J. Srinivasan, Y. Chen, J. Jiang, A. Cable et al., Ultrahigh speed spectral/Fourier domain OCT ophthalmic imaging at 70,000 to 312,500 axial scans per second. Opt. Express 16, 15149–15169 (2008)

    Article  ADS  Google Scholar 

  3. J. Izatt, M. Choma, Theory of optical coherence tomography, in Optical Coherence Tomography (Springer, Berlin, 2008), pp. 47–72

    Chapter  Google Scholar 

  4. M. Szkulmowski, I. Gorczynska, D. Szlag, M. Sylwestrzak, A. Kowalczyk, M. Wojtkowski, Efficient reduction of speckle noise in optical coherence tomography. Opt. Express 20, 1337–1359 (2012)

    Article  ADS  Google Scholar 

  5. M. Bashkansky, J. Reintjes, Statistics and reduction of speckle in optical coherence tomography. Opt. Lett. 25, 545–547 (2000)

    Article  ADS  Google Scholar 

  6. D.D. Duncan, S.J. Kirkpatrick, R.K. Wang, Statistics of local speckle contrast. JOSA A 25, 9–15 (2008)

    Article  ADS  Google Scholar 

  7. J.W. Goodman, Some fundamental properties of speckle. JOSA 66, 1145–1150 (1976)

    Article  ADS  Google Scholar 

  8. B. Karamata, K. Hassler, M. Laubscher, T. Lasser, Speckle statistics in optical coherence tomography. JOSA A 22, 593–596 (2005)

    Article  ADS  MathSciNet  Google Scholar 

  9. J.M. Schmitt, S. Xiang, K.M. Yung, Speckle in optical coherence tomography. J. Biomed. Opt. 4, 95–105 (1999)

    Article  ADS  Google Scholar 

  10. N.M. Grzywacz, J. De Juan, C. Ferrone, D. Giannini, D. Huang, G. Koch et al., Statistics of optical coherence tomography data from human retina. IEEE Trans. Med. Imaging 29, 1224–1237 (2010)

    Article  Google Scholar 

  11. D.H. Ross, Finding Bands in Optical Coherence Tomography Images using Curve and Function Fitting (The University of Alabama at Birmingham, 2014)

    Google Scholar 

  12. A.F. Fercher, W. Drexler, C.K. Hitzenberger, T. Lasser, Optical coherence tomography-principles and applications. Rep. Prog. Phys. 66, 239 (2003)

    Article  ADS  Google Scholar 

  13. N. George, C. Christensen, J. Bennett, B. Guenther, Speckle noise in displays. JOSA 66, 1282–1290 (1976)

    Article  ADS  Google Scholar 

  14. J.W. Goodman, Some effects of target-induced scintillation on optical radar performance. Proc. IEEE 53, 1688–1700 (1965)

    Article  Google Scholar 

  15. R. Loudon, The Quantum Theory of Light (OUP, Oxford, 2000)

    MATH  Google Scholar 

  16. Z. Amini, H. Rabbani, Classification of medical image modeling methods: a review. Curr. Med. Imaging Rev. 12, 130–148 (2016)

    Article  Google Scholar 

  17. I. Jolliffe, Principal Component Analysis (Wiley Online Library, New York, 2002)

    MATH  Google Scholar 

  18. A. Jung, An introduction to a new data analysis tool: Independent component analysis, in Proceedings of Workshop GK” Nonlinearity”-Regensburg (2001)

    Google Scholar 

  19. A. Hyvärinen, E. Oja, Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)

    Article  Google Scholar 

  20. M.P. Arakeri, G.R.M. Reddy, A comparative performance evaluation of independent component analysis in medical image denoising, in 2011 International Conference on Recent Trends in Information Technology (ICRTIT) (2011), pp. 770–774

    Google Scholar 

  21. I. Tosic, P. Frossard, Dictionary learning. Sig. Process. Mag. IEEE 28, 27–38 (2011)

    Article  ADS  MATH  Google Scholar 

  22. R. Kafieh, H. Rabbani, I. Selesnik, Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imaging 34, 1042–1062 (2015)

    Article  Google Scholar 

  23. I. Daubechies, Ten Lectures on Wavelets, vol. 61 (SIAM, Philadelphia, PA, 1992)

    Book  MATH  Google Scholar 

  24. T.F. Chan, J.J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods (SIAM, Philadelphia, PA, 2005)

    Book  MATH  Google Scholar 

  25. M. Jain, S. Sharma, R.M. Sairam, Effect of blur and noise on image denoising based on PDE, in International Journal of Advanced Computer Research (IJACR), vol. 3(1) Issue-8 March-2013 (2013)

    Google Scholar 

  26. H.M. Salinas, D.C. Fernandez, Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imaging 26, 761–771 (2007)

    Article  Google Scholar 

  27. M.K. Garvin, M.D. Abràmoff, R. Kardon, S.R. Russell, X. Wu, M. Sonka, Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Trans. Med. Imaging 27, 1495–1505 (2008)

    Article  Google Scholar 

  28. L.D. Cohen, I. Cohen, Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1131–1147 (1993)

    Article  Google Scholar 

  29. Y. Yu, S. Zhang, K. Li, D. Metaxas, L. Axel, Deformable models with sparsity constraints for cardiac motion analysis. Med. Image Anal. 18, 927–937 (2014)

    Article  Google Scholar 

  30. G. Peyré, Advanced Signal, Image and Surface Processing (University Paris-Dauphine, Ceremade, 2010)

    Google Scholar 

  31. S.J. Wright, R.D. Nowak, M.A. Figueiredo, Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57, 2479–2493 (2009)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  32. H. Rabbani, R. Nezafat, S. Gazor, Wavelet-domain medical image denoising using bivariate Laplacian mixture model. IEEE Trans. Biomed. Eng. 56, 2826–2837 (2009)

    Article  Google Scholar 

  33. R.R. Coifman, M. Maggioni, Diffusion wavelets. Appl. Comput. Harmonic Anal. 21, 53–94 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. D. Cabrera Fernández, N. Villate, C. Puliafito, P. Rosenfeld, Comparing total macular volume changes measured by optical coherence tomography with retinal lesion volume estimated by active contours. Invest. Ophtalmol. Vis. Sci. 45, 3072 (2004)

    Google Scholar 

  35. G. Gregori, R. Knighton, A robust algorithm for retinal thickness measurements using optical coherence tomography (Stratus OCT). Invest. Ophtalmol. Vis. Sci. 45, 3007 (2004)

    Google Scholar 

  36. J. Rogowska, M.E. Brezinski, Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging. IEEE Trans. Med. Imaging 19, 1261–1266 (2000)

    Article  Google Scholar 

  37. A.M. Bagci, M. Shahidi, R. Ansari, M. Blair, N.P. Blair, R. Zelkha, Thickness profiles of retinal layers by optical coherence tomography image segmentation. Am. J. Ophthalmol. 146, 679–687 (2008). (e1)

    Article  Google Scholar 

  38. R. Bernardes, C. Maduro, P. Serranho, A. Araújo, S. Barbeiro, J. Cunha-Vaz, Improved adaptive complex diffusion despeckling filter. Opt. Express 18, 24048–24059 (2010)

    Article  ADS  Google Scholar 

  39. A.R. Fuller, R.J. Zawadzki, S. Choi, D.F. Wiley, J.S. Werner, B. Hamann, Segmentation of three-dimensional retinal image data. IEEE Trans. Visual Comput. Graphics 13, 1719–1726 (2007)

    Article  Google Scholar 

  40. A. Mishra, A. Wong, K. Bizheva, D.A. Clausi, Intra-retinal layer segmentation in optical coherence tomography images. Opt. Express 17, 23719–23728 (2009)

    Article  ADS  Google Scholar 

  41. F. Luan, Y. Wu, Application of RPCA in optical coherence tomography for speckle noise reduction. Laser Phys. Lett. 10, 035603 (2013)

    Article  ADS  Google Scholar 

  42. V. Gupta, C.C. Chan, C.-L. Poh, T.H. Chow, T.C. Meng, N.B. Koon, Computerized automation of wavelet based denoising method to reduce speckle noise in OCT images, in International Conference on Information Technology and Applications in Biomedicine, ITAB (2008), pp. 120–123

    Google Scholar 

  43. M.A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C.Y. Mardin, R.P. Tornow, Wavelet denoising of multiframe optical coherence tomography data. Biomed. Opt. Express 3, 572–589 (2012)

    Article  Google Scholar 

  44. Z. Jian, Z. Yu, L. Yu, B. Rao, Z. Chen, B.J. Tromberg, Speckle attenuation in optical coherence tomography by curvelet shrinkage. Opt. Lett. 34, 1516–1518 (2009)

    Article  ADS  Google Scholar 

  45. S. Chitchian, M.A. Fiddy, N.M. Fried, Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform. J. Biomed. Opt.cs 14, 014031–014031-6 (2009)

    Article  ADS  Google Scholar 

  46. V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P.L. Rosin, W. Drexler, Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model. Biomed. Opt. Express 3, 86–103 (2012)

    Article  Google Scholar 

  47. V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P.L. Rosin et al., Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Opt. Express 18, 14730–14744 (2010)

    Article  ADS  Google Scholar 

  48. R. Kafieh, H. Rabbani, M.D. Abramoff, M. Sonka, Curvature correction of retinal OCTs using graph-based geometry detection. Phys. Med. Biol. 58, 2925 (2013)

    Article  Google Scholar 

  49. Z. Amini, H. Rabbani, Statistical modeling of retinal optical coherence tomography. IEEE Trans. Med. Imaging 35, 1544–1554 (2016)

    Article  Google Scholar 

  50. A. Achim, A. Bezerianos, P. Tsakalides, Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20, 772–783 (2001)

    Article  Google Scholar 

  51. A. Pizurica, L. Jovanov, B. Huysmans, V. Zlokolica, P. De Keyser, F. Dhaenens et al., Multiresolution denoising for optical coherence tomography: a review and evaluation. Curr. Med. Imaging Rev. 4, 270–284 (2008)

    Article  Google Scholar 

  52. S.S. Agaian, B. Silver, K.A. Panetta, Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16, 741–758 (2007)

    Article  ADS  MathSciNet  Google Scholar 

  53. J. Zhou, A.L. Cunha, M.N. Do, Nonsubsampled contourlet transform: construction and application in enhancement, in IEEE International Conference on Image Processing 2005 (2005), pp. I-469–72

    Google Scholar 

  54. L. Fang, S. Li, Q. Nie, J.A. Izatt, C.A. Toth, S. Farsiu, Sparsity based denoising of spectral domain optical coherence tomography images. Biomed. Opt. Express 3, 927–942 (2012)

    Article  Google Scholar 

  55. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process. 18, 1438–1451 (2009)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  56. M. Aharon, M. Elad, A. Bruckstein, k -SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311–4322 (2006)

    Article  ADS  MATH  Google Scholar 

  57. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)

    Article  ADS  MathSciNet  Google Scholar 

  58. Y.C. Pati, R. Rezaiifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in 1993 Conference Record of the Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, vol.1 (1993), pp. 40–44

    Google Scholar 

  59. N. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmonic Anal. 10, 234–253 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  60. I.W. Selesnick, R.G. Baraniuk, N.C. Kingsbury, The dual-tree complex wavelet transform. Sig. Process. Mag. IEEE 22, 123–151 (2005)

    Article  ADS  Google Scholar 

  61. I.W. Selesnick, K.Y. Li, Video denoising using 2D and 3D dual-tree complex wavelet transforms. Wavelets Appl. Sign. Image Process. X 5207, 607–618 (2003)

    ADS  Google Scholar 

  62. P. Puvanathasan, K. Bizheva, Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images. Opt. Express 17, 733–746 (2009)

    Article  ADS  Google Scholar 

  63. D.C. Adler, T.H. Ko, J.G. Fujimoto, Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter. Opt. Lett. 29, 2878–2880 (2004)

    Article  ADS  Google Scholar 

  64. V. Zlokolica, L. Jovanov, A. Pizurica, P. De Keyser, F. Dhaenens, W. Philips, Wavelet-based denoising for 3D OCT images, in Proceedings of SPIE (2007), p. 66960P

    Google Scholar 

  65. Z. Jian, L. Yu, B. Rao, B.J. Tromberg, Z. Chen, Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform. Opt. Express 18, 1024–1032 (2010)

    Article  ADS  Google Scholar 

  66. S. Gupta, R.C. Chauhan, S.C. Saxena, Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Compu. 43(2), 189–195 (2005)

    Article  Google Scholar 

  67. S. Gupta, L. Kaur, R.C. Chauhan, S.C. Saxena, A versatile technique for visual enhancement of medical ultrasound images. Digit. Signal Proc. 17(3), 542–560 (2007)

    Article  Google Scholar 

  68. S. Yan, J. Yuan, M. Liu, C. Hou, Speckle noise reduction of ultrasound images based on an undecimated wavelet packet transform domain nonhomomorphic filtering, in Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (October 2009), pp. 1–5

    Google Scholar 

  69. H. Rabbani, M. Sonka, M.D. Abramoff, Optical coherence tomography noise reduction using anisotropic local bivariate gaussian mixture prior in 3-D complex wavelet domain. Int. J. Biomed. Imaging 2013, Article ID 417491, 23 p (2013)

    Google Scholar 

  70. V. Katkovnik, K. Egiazarian, J. Astola, Adaptive window size image de-noising based on intersection of confidence intervals (ICI) rule. J. Math. Imaging Vis. 16(3), 223–235 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  71. A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, G.J. Tearney, Speckle reduction in optical coherence tomography images using digital filtering. JOSA A 24, 1901–1910 (2007)

    Article  ADS  Google Scholar 

  72. G.A. Campbell, R.M. Foster, Fourier Integrals for Practical Applications (Bell telephone laboratories, New York, 1948)

    MATH  Google Scholar 

  73. N. Ahmed, T. Natarajan, K.R. Rao, Discrete cosine transform. IEEE Trans. Comput. 100, 90–93 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  74. R.R. Coifman, M.V. Wickerhauser, Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 38, 713–718 (1992)

    Article  MATH  Google Scholar 

  75. D.L. Donoho, Wedgelets: nearly minimax estimation of edges. Ann. Stat. 27, 859–897 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  76. N.G. Kingsbury, The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters, in Proceedings 8th IEEE DSP Workshop, Utah (1998), p. 86

    Google Scholar 

  77. E. Candes, L. Demanet, D. Donoho, L. Ying, Fast discrete curvelet transforms. Multiscale Model. Simul. 5, 861–899 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  78. E. Le Pennec, S. Mallat, Bandelet image approximation and compression. Multiscale Model. Simul. 4, 992–1039 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  79. M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14, 2091–2106 (2005)

    Article  ADS  Google Scholar 

  80. H. Chauris, I. Karoui, P. Garreau, H. Wackernagel, P. Craneguy, L. Bertino, The circlet transform: a robust tool for detecting features with circular shapes. Comput. Geosci. 37, 331–342 (2011)

    Article  ADS  Google Scholar 

  81. Y. Lu, M.N. Do, 3-D directional filter banks and surfacelets, in SPIE Optics & Photonics (2005), p. 59141Q

    Google Scholar 

  82. P. Comon, Independent component analysis, a new concept? Sig. Process. 36, 287–314 (1994)

    Article  ADS  MATH  Google Scholar 

  83. K. Engan, S.O. Aase, J. Husoy, Frame based signal compression using method of optimal directions (MOD), in IEEE International Symposium on Circuits and Systems (1999), pp. 1–4

    Google Scholar 

  84. N. Iftimia, B.E. Bouma, G.J. Tearney, Speckle reduction in optical coherence tomography by “path length encoded” angular compounding. J. Biomed. Opt. 8, 260–263 (2003)

    Article  ADS  Google Scholar 

  85. L. Ramrath, G. Moreno, H. Mueller, T. Bonin, G. Huettmann, A. Schweikard, Towards multi-directional OCT for speckle noise reduction, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008 (Springer, Berlin, 2008), pp. 815–823

    Chapter  Google Scholar 

  86. M. Hughes, M. Spring, A. Podoleanu, Speckle noise reduction in optical coherence tomography of paint layers. Appl. Opt. 49, 99–107 (2010)

    Article  ADS  Google Scholar 

  87. A. Desjardins, B. Vakoc, G. Tearney, B. Bouma, Speckle reduction in OCT using massively-parallel detection and frequency-domain ranging. Opt. Express 14, 4736–4745 (2006)

    Article  ADS  Google Scholar 

  88. M. Pircher, E. Go, R. Leitgeb, A.F. Fercher, C.K. Hitzenberger, Speckle reduction in optical coherence tomography by frequency compounding. J. Biomed. Opt. 8, 565–569 (2003)

    Article  ADS  Google Scholar 

  89. B. Sander, M. Larsen, L. Thrane, J. Hougaard, T. Jørgensen, Enhanced optical coherence tomography imaging by multiple scan averaging. Br. J. Ophthalmol. 89, 207–212 (2005)

    Article  Google Scholar 

  90. E. Götzinger, M. Pircher, C.K. Hitzenberger, High speed spectral domain polarization sensitive optical coherence tomography of the human retina. Opt. Express 13, 10217–10229 (2005)

    Article  ADS  Google Scholar 

  91. T.M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, B. Sander, Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples. J. Biomed. Opt. 12, 041208–041210 (2007)

    Article  ADS  Google Scholar 

  92. R.D. Ferguson, D.X. Hammer, L.A. Paunescu, S. Beaton, J.S. Schuman, Tracking optical coherence tomography. Opt. Lett. 29, 2139–2141 (2004)

    Article  ADS  Google Scholar 

  93. M.R. Hee, J.A. Izatt, E.A. Swanson, D. Huang, J.S. Schuman, C.P. Lin et al., Optical coherence tomography of the human retina. Arch. Ophthalmol. 113, 325–332 (1995)

    Article  Google Scholar 

  94. A. George, J. Dillenseger, A. Weber, A. Pechereau, Optical coherence tomography image processing. Investigat. Ophthalmol. Vis. Sci. 41, 165–173 (2000)

    Google Scholar 

  95. D. Koozekanani, K. Boyer, C. Roberts, Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans. Med. Imaging 20, 900–916 (2001)

    Article  Google Scholar 

  96. J. Rogowska, M.E. Brezinski, Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images. Phys. Med. Biol. 47, 641 (2002)

    Article  Google Scholar 

  97. M. Shahidi, Z. Wang, R. Zelkha, Quantitative thickness measurement of retinal layers imaged by optical coherence tomography. Am. J. Ophthalmol. 139, 1056–1061 (2005)

    Article  Google Scholar 

  98. K.L. Boyer, A. Herzog, C. Roberts, Automatic recovery of the optic nervehead geometry in optical coherence tomography. IEEE Trans. Med. Imaging 25, 553–570 (2006)

    Article  Google Scholar 

  99. V.J. Srinivasan, B.K. Monson, M. Wojtkowski, R.A. Bilonick, I. Gorczynska, R. Chen et al., Characterization of outer retinal morphology with high-speed, ultrahigh-resolution optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 49, 1571–1579 (2008)

    Article  Google Scholar 

  100. K. Lee, M.D. Abràmoff, M. Niemeijer, M.K. Garvin, M. Sonka, 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head, Presented at the Proceedings of SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging (2010)

    Google Scholar 

  101. H. Ishikawa, D.M. Stein, G. Wollstein, S. Beaton, J.G. Fujimoto, J.S. Schuman, Macular segmentation with optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 46, 2012–2017 (2005)

    Article  Google Scholar 

  102. M. Mayer, R. Tornow, R. Bock, J. Hornegger, F. Kruse, Automatic nerve fiber layer segmentation and geometry correction on spectral domain OCT images using fuzzy C-means clustering. Invest. Ophthalmol. Vis. Sci. 49, pp. E-Abstract 1880 (2008)

    Google Scholar 

  103. M. Baroni, P. Fortunato, A. La Torre, Towards quantitative analysis of retinal features in optical coherence tomography. Med. Eng. Phys. 29, 432–441 (2007)

    Article  Google Scholar 

  104. D.L. Marks, T.S. Ralston, S.A. Boppart, Speckle reduction by I-divergence regularization in optical coherence tomography. JOSA A 22, 2366–2371 (2005)

    Article  ADS  Google Scholar 

  105. A. Wong, A. Mishra, K. Bizheva, D.A. Clausi, General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery. Opt. Express 18, 8338–8352 (2010)

    Article  ADS  Google Scholar 

  106. F. Leyuan, L. Shutao, R.P. McNabb, N. Qing, A.N. Kuo, C.A. Toth et al., Fast acquisition and reconstruction of optical coherence tomography images via sparse representation. IEEE Trans. Med. Imaging 32, 2034–2049 (2013)

    Article  Google Scholar 

  107. G. Quellec, K. Lee, M. Dolejsi, M.K. Garvin, M.D. Abràmoff, M. Sonka, Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula. IEEE Trans. Med. Imaging 29, 1321–1330 (2010)

    Article  Google Scholar 

  108. M. Forouzanfar, H. Moghaddam, A directional multiscale approach for speckle reduction in optical coherence tomography images, in IEEE International Conference on Electrical Engineering, ICEE’07, Lahore (2007), pp. 1–6

    Google Scholar 

  109. M.D. Abràmoff, K. Lee, M. Niemeijer, W.L. Alward, E.C. Greenlee, M.K. Garvin et al., Automated segmentation of the cup and rim from spectral domain OCT of the optic nerve head. Invest. Ophthalmol. Vis. Sci. 50, 5778–5784 (2009)

    Article  Google Scholar 

  110. A. Yazdanpanah, G. Hamarneh, B. Smith, M. Sarunic, Intra-retinal layer segmentation in optical coherence tomography using an active contour approach, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009 (Springer, Berlin, 2009), pp. 649–656

    Chapter  Google Scholar 

  111. Q. Yang, C.A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura et al., Automated layer segmentation of macular OCT images using dual-scale gradient information. Opt. Express 18, 21293 (2010)

    Article  ADS  Google Scholar 

  112. R. Kafieh, H. Rabbani, M.D. Abramoff, M. Sonka, Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med. Image Anal. (2013)

    Google Scholar 

  113. H. Bogunovic, M. Sonka, Y. Kwon, P. Kemp, M. Abramoff, X. Wu, Multi-surface and multi-field co-segmentation of 3-D retinal optical coherence tomography. IEEE Trans. Med. Imaging 99, 247–253 (2014)

    Google Scholar 

  114. F. Rathke, S. Schmidt, C. Schnörr, Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization. Med. Image Anal. 18, 781–794 (2014)

    Article  Google Scholar 

  115. D.J. George A, A. Weber, A. Pechereau, Optical coherence tomography image processing. Investigat. Ophthalmol. Vis. Sci. 41, 165–173 (2000)

    Google Scholar 

  116. A. Herzog, K.L. Boyer, C. Roberts, Robust extraction of the optic nerve head in optical coherence tomography, in Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis (Springer, Berlin, 2004), pp. 395–407

    Google Scholar 

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Authors and Affiliations

  1. Department of Bioelectrics & Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Zahra Amini, Raheleh Kafieh & Hossein Rabbani

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  1. Zahra Amini
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  2. Raheleh Kafieh
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  3. Hossein Rabbani
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Correspondence to Hossein Rabbani .

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  1. School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, China

    Xinjian Chen

  2. School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, China

    Fei Shi

  3. Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, China

    Haoyu Chen

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© 2019 Science Press and Springer Nature Singapore Pte Ltd.

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Amini, Z., Kafieh, R., Rabbani, H. (2019). Speckle Noise Reduction and Enhancement for OCT Images. In: Chen, X., Shi, F., Chen, H. (eds) Retinal Optical Coherence Tomography Image Analysis. Biological and Medical Physics, Biomedical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-1825-2_3

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