Abstract

Optical coherence tomography (OCT) allows for non-invasive 3D visualization of biological tissue at cellular level resolution. Often hindered by speckle noise, the visualization of important biological tissue details in OCT that can aid disease diagnosis can be improved by speckle noise compensation. A challenge with handling speckle noise is its inherent non-stationary nature, where the underlying noise characteristics vary with the spatial location. In this study, an innovative speckle noise compensation method is presented for handling the non-stationary traits of speckle noise in OCT imagery. The proposed approach centers on a non-stationary spline-based speckle noise modeling strategy to characterize the speckle noise. The novel method was applied to ultra high-resolution OCT (UHROCT) images of the human retina and corneo-scleral limbus acquired in-vivo that vary in tissue structure and optical properties. Test results showed improved performance of the proposed novel algorithm compared to a number of previously published speckle noise compensation approaches in terms of higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and better overall visual assessment.

© 2013 OSA

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  1. B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
    [CrossRef]
  2. T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser,” Opt. Express19, 3044–3062 (2011).
    [CrossRef] [PubMed]
  3. W. Drexler and J. Fujimoto, “Biological and medical physics, biomedical engineering,” in Optical Coherence Tomography: Technology and Applications (Springer, 2008).
    [CrossRef]
  4. J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” 19, 1261–1266 (2000).
  5. J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol.42, 1427–1439 (1997).
    [CrossRef] [PubMed]
  6. M. Bashkansky and J. Reintjes, “Statistics and reduction of speckle in optical coherence tomography,” Opt. Lett.25, 545–547 (2000).
    [CrossRef]
  7. N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt.8, 260–263 (2003).
    [CrossRef] [PubMed]
  8. A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
    [CrossRef] [PubMed]
  9. T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.
  10. D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
    [CrossRef]
  11. L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express3, 927–942 (2012).
    [CrossRef] [PubMed]
  12. J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng.25, 636–643 (1986).
    [CrossRef]
  13. V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
    [CrossRef]
  14. D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
    [CrossRef]
  15. T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
    [CrossRef]
  16. A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express18, 8338–8352 (2010).
    [CrossRef] [PubMed]
  17. A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
    [CrossRef]
  18. D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett.29, 2878–2880 (2004).
    [CrossRef]
  19. A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” Opt. Lett.24, 1901–1910 (2007).
  20. P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express15, 15747–15758 (2007).
    [CrossRef] [PubMed]
  21. M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express16, 12313–12333 (2008).
    [CrossRef] [PubMed]
  22. Z. Jian, L. Yu, B. Rao, B. J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform,” Opt. Express18, 1024–1032 (2010).
    [CrossRef] [PubMed]
  23. Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express11, 1260–1270 (2002).
  24. D. Fernandez, H. Salinas, and C. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express13, 10200–10216 (2005).
    [CrossRef]
  25. R. Bernardes, C. Maduro, P. Serranho, A. Araujo, S. Barberio, and J. Cunha-Vas, “Improved adaptive complex diffusion despeckling filter,” Opt. Express18, 24048–24059 (2010).
    [CrossRef] [PubMed]
  26. D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
    [CrossRef]
  27. R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Trans Geosci. Remote Sens.40, 2392–2404 (2002).
    [CrossRef]
  28. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
    [CrossRef]
  29. J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
    [CrossRef] [PubMed]
  30. A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 (IEEE, 2005), pp. 60–65.
  31. A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.
  32. J. A. Fessler, “Tomographic reconstruction using information-weighted spline smoothing,” in Information Processing in Medical Imaging,H. H. Barrett and A. F. Gmitro, eds. (Springer BerlinHeidelberg, 1993), pp. 372–386.
    [CrossRef]
  33. P. Fieguth, Statistical Image Processing and Multidimensional Modeling (Springer Science+Business Media, New York, 2011), chap. 3, p. 65.
  34. M.-H. Chen, “Importance-weighted marginal Bayesian posterior density estimation,” J. Am. Stat. Assoc.89, 818–824 (1994).
    [CrossRef]
  35. F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
    [CrossRef] [PubMed]
  36. S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process.15, 2694–2701 (2006).
    [CrossRef] [PubMed]

2012

2011

2010

2009

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

2008

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express16, 12313–12333 (2008).
[CrossRef] [PubMed]

J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
[CrossRef] [PubMed]

2007

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
[CrossRef] [PubMed]

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
[CrossRef]

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

P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express15, 15747–15758 (2007).
[CrossRef] [PubMed]

2006

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process.15, 2694–2701 (2006).
[CrossRef] [PubMed]

2005

2004

2003

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

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

2002

R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Trans Geosci. Remote Sens.40, 2392–2404 (2002).
[CrossRef]

Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express11, 1260–1270 (2002).

2000

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

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” 19, 1261–1266 (2000).

1997

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol.42, 1427–1439 (1997).
[CrossRef] [PubMed]

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

1994

M.-H. Chen, “Importance-weighted marginal Bayesian posterior density estimation,” J. Am. Stat. Assoc.89, 818–824 (1994).
[CrossRef]

1993

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

1989

T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
[CrossRef]

1987

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

1986

J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng.25, 636–643 (1986).
[CrossRef]

1985

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

1982

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

Acton, S.

Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express11, 1260–1270 (2002).

Adler, D. C.

Aja-Fernández, S.

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process.15, 2694–2701 (2006).
[CrossRef] [PubMed]

Alberola-López, C.

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process.15, 2694–2701 (2006).
[CrossRef] [PubMed]

Allen, P.

T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
[CrossRef]

Andersen, P. E.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

Araujo, A.

Barberio, S.

Bashkansky, M.

Bernardes, R.

Biedermann, B. R.

Bilenca, A.

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

Bizheva, K.

Bouma, B. E.

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

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
[CrossRef] [PubMed]

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

Brezinski, M. E.

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” 19, 1261–1266 (2000).

Buades, A.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 (IEEE, 2005), pp. 60–65.

Cable, A.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Chavel, P.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

Chen, M.-H.

M.-H. Chen, “Importance-weighted marginal Bayesian posterior density estimation,” J. Am. Stat. Assoc.89, 818–824 (1994).
[CrossRef]

Chen, Y.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Chen, Z.

Clausi, D. A.

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express18, 8338–8352 (2010).
[CrossRef] [PubMed]

A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.

Coll, B.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 (IEEE, 2005), pp. 60–65.

Cunha-Vas, J.

Desjardins, A. E.

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

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
[CrossRef] [PubMed]

Drexler, W.

W. Drexler and J. Fujimoto, “Biological and medical physics, biomedical engineering,” in Optical Coherence Tomography: Technology and Applications (Springer, 2008).
[CrossRef]

Duker, J. S.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Eigenwillig, C. M.

Fang, L.

Farsiu, S.

Fernandez, D.

Fessler, J. A.

J. A. Fessler, “Tomographic reconstruction using information-weighted spline smoothing,” in Information Processing in Medical Imaging,H. H. Barrett and A. F. Gmitro, eds. (Springer BerlinHeidelberg, 1993), pp. 372–386.
[CrossRef]

Fieguth, P.

P. Fieguth, Statistical Image Processing and Multidimensional Modeling (Springer Science+Business Media, New York, 2011), chap. 3, p. 65.

A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.

Floreby, L.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

Frost, V.

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

Fujimoto, J.

W. Drexler and J. Fujimoto, “Biological and medical physics, biomedical engineering,” in Optical Coherence Tomography: Technology and Applications (Springer, 2008).
[CrossRef]

Fujimoto, J. G.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

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

Gargesha, M.

Gorczynska, I.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Guerrero-Colón, J.-A.

J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
[CrossRef] [PubMed]

Hewko, M. D.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
[CrossRef]

Holtzman, J.

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

Huber, R.

Iftimia, N.

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

Izatt, J. A.

Jenkins, M. W.

Jian, Z.

Jiang, J.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Jørgensen, T. M.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

Klein, T.

Ko, T. H.

Kuan, D.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

Kuan, D. T.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

Laur, H.

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

Lee, J.

J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng.25, 636–643 (1986).
[CrossRef]

Leigh, A.

A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.

Li, S.

Liu, J.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Lopes, A.

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

Loupas, T.

T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
[CrossRef]

Lovstrom, B.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

Maduro, C.

Mancera, L.

J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
[CrossRef] [PubMed]

Mcdicken, W.

T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
[CrossRef]

Mishra, A.

Mogensen, M.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

Morel, J. M.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 (IEEE, 2005), pp. 60–65.

Motaghiannezam, S. M.

Nezry, E.

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

Nie, Q.

Oh, W. Y.

Ozcan, A.

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

Pedersen, F.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

Popescu, D. P.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
[CrossRef]

Portilla, J.

J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
[CrossRef] [PubMed]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

Potsaid, B.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Puliafito, C.

Puvanathasan, P.

Rao, B.

Reintjes, J.

Rogowska, J.

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” 19, 1261–1266 (2000).

Rollins, A. M.

Salinas, H.

Salomonsson, G.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

Sattar, F.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

Sawchuk, A.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

Sawchuk, A. A.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

Schmitt, J. M.

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol.42, 1427–1439 (1997).
[CrossRef] [PubMed]

Serranho, P.

Shanmugan, K.

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

Simoncelli, E. P.

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

Sowa, M. G.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
[CrossRef]

Srinivasan, V. J.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Stiles, J.

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

Strand, T.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

Strand, T. C.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

Strela, V.

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

Tearney, G. J.

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
[CrossRef] [PubMed]

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

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

Thrane, L.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

Toth, C. A.

Touzi, R.

R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Trans Geosci. Remote Sens.40, 2392–2404 (2002).
[CrossRef]

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

Tromberg, B. J.

Vakoc, B. J.

Wainwright, M. J.

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

Wieser, W.

Wilson, D. L.

Wong, A.

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express18, 8338–8352 (2010).
[CrossRef] [PubMed]

A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.

Yu, L.

Yu, Y.

Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express11, 1260–1270 (2002).

Biomed. Opt. Express

IEEE Trans Geosci. Remote Sens.

R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Trans Geosci. Remote Sens.40, 2392–2404 (2002).
[CrossRef]

IEEE Trans. Acoust. Speech Signal Process.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process.35, 373–383 (1987).
[CrossRef]

IEEE Trans. Circuits Syst.

T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst.36, 129–135 (1989).
[CrossRef]

IEEE Trans. Image Process.

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process.23, 1338–1351 (2003).
[CrossRef]

J.-A. Guerrero-Colón, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process.17, 27–41 (2008).
[CrossRef] [PubMed]

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process.6, 888–895 (1997).
[CrossRef] [PubMed]

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process.15, 2694–2701 (2006).
[CrossRef] [PubMed]

IEEE Trans. Pattern Analysis Mach. Intell.

V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering for multiplicative noise,” IEEE Trans. Pattern Analysis Mach. Intell.4, 157–166 (1982).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Analysis Mach. Intell.7, 165–177 (1985).
[CrossRef]

Int. J. Remote Sens.

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens.14, 1735–1758 (1993).
[CrossRef]

J. Am. Stat. Assoc.

M.-H. Chen, “Importance-weighted marginal Bayesian posterior density estimation,” J. Am. Stat. Assoc.89, 818–824 (1994).
[CrossRef]

J. Biomed. Opt.

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

Opt. Commun.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun.269, 247–251 (2007).
[CrossRef]

Opt. Eng.

J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng.25, 636–643 (1986).
[CrossRef]

Opt. Express

Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express11, 1260–1270 (2002).

D. Fernandez, H. Salinas, and C. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express13, 10200–10216 (2005).
[CrossRef]

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express15, 6200–6209 (2007).
[CrossRef] [PubMed]

P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express15, 15747–15758 (2007).
[CrossRef] [PubMed]

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express16, 12313–12333 (2008).
[CrossRef] [PubMed]

Z. Jian, L. Yu, B. Rao, B. J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform,” Opt. Express18, 1024–1032 (2010).
[CrossRef] [PubMed]

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express18, 8338–8352 (2010).
[CrossRef] [PubMed]

R. Bernardes, C. Maduro, P. Serranho, A. Araujo, S. Barberio, and J. Cunha-Vas, “Improved adaptive complex diffusion despeckling filter,” Opt. Express18, 24048–24059 (2010).
[CrossRef] [PubMed]

T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser,” Opt. Express19, 3044–3062 (2011).
[CrossRef] [PubMed]

Opt. Lett.

Phys. Med. Biol.

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol.42, 1427–1439 (1997).
[CrossRef] [PubMed]

Proc. SPIE

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Liu, J. Jiang, A. Cable, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed spectral / Fourier domain opthalmic OCT imaging,” Proc. SPIE7163, 716307 (2009).
[CrossRef]

Other

W. Drexler and J. Fujimoto, “Biological and medical physics, biomedical engineering,” in Optical Coherence Tomography: Technology and Applications (Springer, 2008).
[CrossRef]

J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” 19, 1261–1266 (2000).

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Optical Coherence Tomography and Coherence Techniques III, vol. 6627 of Proceedings of SPIE-OSA Biomedical Optics, P. Andersen and Z. Chen, eds. (Optical Society of America, 2007), pp. 22.

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 (IEEE, 2005), pp. 60–65.

A. Leigh, A. Wong, D. A. Clausi, and P. Fieguth, “Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance,” in Proceedings of IEEE International Symposium on Multimedia(IEEE, 2011), pp. 97–104.

J. A. Fessler, “Tomographic reconstruction using information-weighted spline smoothing,” in Information Processing in Medical Imaging,H. H. Barrett and A. F. Gmitro, eds. (Springer BerlinHeidelberg, 1993), pp. 372–386.
[CrossRef]

P. Fieguth, Statistical Image Processing and Multidimensional Modeling (Springer Science+Business Media, New York, 2011), chap. 3, p. 65.

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Figures (4)

Fig. 1
Fig. 1

UHROCT imagery of (A) a healthy human retina, (B) a healthy human corneo-scleral limbus and (C) a human limbus with pinguecula, acquired in-vivo. The blue boxes in these images mark the homogeneous regions of interest (ROIs) used for calculation of the SNR value, while the red boxes are pairs of ROIs that were used to obtain the CNR values.

Fig. 2
Fig. 2

Results of applying different speckle compensation methods on the human retina imagery.

Fig. 3
Fig. 3

Results of applying different speckle compensation methods on the healthy human limbus imagery.

Fig. 4
Fig. 4

Results of applying different speckle compensation methods on the human limbus with pinguecula imagery.

Tables (5)

Tables Icon

Table 1 Average SNR values taken over ROIs in the retina (R), healthy human limbus (HL), and human limbus with pinguecula (HLwP) imagery. The highest value for each type of imagery is bolded. IACD and SRAD resulted in some high SNR values but at the cost of overcompensation (see Figs. 2 and 4). BLSGSM and NSC had very close improvements to SNR while other methods had less drastic changes.

Tables Icon

Table 2 Average CNR values taken over ROIs in the retina (R), healthy human limbus (HL), and human limbus with pinguecula (HLwP) imagery. NSC achieved the best contrast improvement in each type of imagery while maintaining structure.

Tables Icon

Table 3 Average ENL values taken over ROIs in the retina (R), healthy human limbus (HL), and human limbus with pinguecula (HLwP) imagery. NSC achieved the best improvements to ENL without smoothing structures and detail (see Figs. 2 and 4).

Tables Icon

Table 4 Edge preservation values for the retina (R), healthy human limbus (HL), and human limbus with pinguecula (HLwP) imagery. Although BLSGSM achieved higher values, NSC did a better job of balancing speckle suppression with edge preservation (see Figs. 2 and 4).

Tables Icon

Table 5 Run-times for the various compared methods on each type of imagery.

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

G ( x _ ) = F ( x _ ) N ( x _ ) ,
log G ( x _ ) = log F ( x _ ) + log N ( x _ ) .
log F ^ ( x _ ) = argmin E log F ( x _ ) ( ( log F ( x _ ) log F ^ ( x _ ) ) 2 | log G ( x _ ) ) = argmin log F ( x _ ) ( log F ( x _ ) log F ^ ( x _ ) ) 2 p ( log F ( x _ ) | log G ( x _ ) ) d ( log F ( x _ ) ) .
σ ( x _ ) = 1.4826 median N ( | log G ( x _ i ) median N ( log G ) | ) .
M ( x _ ) = mode S ( σ )
S ( x _ ) = argmin S ( x _ ) p j = 1 n ( M ( x _ j ) S ( x _ j ) ) 2 + ( 1 p ) D 2 S ( x _ ) 2 d x _ ,
α ( x _ i | x _ 0 ) = Π j ( 2 π S ( x _ 0 ) ) 1 / 2 exp [ ( h i [ j ] h 0 [ j ] ) 2 2 S ( x _ 0 ) ] Π j exp λ j .
S N R = 1 R [ r = 1 R 10 log 10 ( μ r 2 σ r 2 ) ] ,
C N R = 1 R [ r = 1 R 10 log 10 ( μ r 1 μ r 2 ) σ r 1 2 + σ r 2 2 ] ,
E N L = 1 H [ h = 1 H μ h 2 σ h 2 ]
η = Σ ( 2 V 2 V ¯ ) ( 2 G ^ 2 G ^ ¯ ) Σ ( 2 V 2 V ¯ ) 2 Σ ( 2 G ^ 2 G ^ ¯ ) 2

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