Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling

Biomedical Optics Express

Volumn 4, Issue 9, 2013, Pages 1769-1785

  Cameron, Andrew ^a   Lui, Dorothy ^a   Boroomand, Ameneh ^a   Glaister, Jeffrey ^a   Wong, Alexander ^a   Bizheva, Kostadinka ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DIAGNOSIS; OPTICAL TOMOGRAPHY; SPECKLE; THREE DIMENSIONAL COMPUTER GRAPHICS; VISUALIZATION;

3D VISUALIZATION; BIOLOGICAL TISSUES; CELLULAR-LEVEL RESOLUTION; CONTRAST TO NOISE RATIO; DISEASE DIAGNOSIS; NOISE CHARACTERISTIC; TISSUE STRUCTURE; VISUAL ASSESSMENTS;

SIGNAL TO NOISE RATIO;

ALGORITHM; ARTICLE; CONJUNCTIVAL DEPOSIT; CONTRAST TO NOISE RATIO; CORNEA LIMBUS; HUMAN; HUMAN EXPERIMENT; IMAGING AND DISPLAY; NOISE REDUCTION; NONSTATIONARY SPECKLE COMPENSATION METHOD; OPTICAL COHERENCE TOMOGRAPHY; RETINA; SIGNAL NOISE RATIO; SPECKLE NOISE; STOCHASTIC MODEL; WAVELET ANALYSIS;

EID: 84883393490     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.4.001769     Document Type: Article

References (36)

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. SPIE 7163, 716307 (2009).
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. Express 19, 3044-3062 (2011).
3. W. Drexler and J. Fujimoto, “Biological and medical physics, biomedical engineering,” in Optical Coherence Tomography: Technology and Applications (Springer, 2008).
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).
6. M. Bashkansky and J. Reintjes, “Statistics and reduction of speckle in optical coherence tomography,” Opt. Lett. 25, 545-547 (2000).
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).
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. Express 15, 6200-6209 (2007).
9. T. M. Jorgensen, 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).
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. Express 3, 927-942 (2012).
12. J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng. 25, 636-643 (1986).
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).
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).
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).
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. Express 18, 8338-8352 (2010).
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).
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).
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. Express 15, 15747-15758 (2007).
21. M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16, 12313-12333 (2008).
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. Express 18, 1024-1032 (2010).
23. Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” Opt. Express 11, 1260-1270 (2002).
24. D. Fernandez, H. Salinas, and C. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13, 10200-10216 (2005).
25. R. Bernardes, C. Maduro, P. Serranho, A. Araujo, S. Barberio, and J. Cunha-Vas, “Improved adaptive complex diffusion despeckling filter,” Opt. Express 18, 24048-24059 (2010).
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).
27. R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Trans Geosci. Remote Sens. 40, 2392-2404 (2002).
28. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures ofGaussians in the wavelet domain,” IEEE Trans. Image Process. 23, 1338-1351 (2003).
29. J.-A. Guerrero-Colon, L. Mancera, and J. Portilla, “Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids,” IEEE Trans. Image Process. 17, 27-41 (2008).
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 Berlin Heidelberg, 1993), pp. 372-386.
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).
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).
36. S. Aja-Fernandez and C. Alberola-Lopez, “On the estimation of the coefficient of variation for anisotropic diffu.