## General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery

Optics Express, Vol. 18, Issue 8, pp. 8338-8352 (2010)

http://dx.doi.org/10.1364/OE.18.008338

Acrobat PDF (1951 KB)

### Abstract

An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired *in-vivo* with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.

© 2010 Optical Society of America

## 1. Introduction

1. J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging **19**, 1261–1266 (2000). [CrossRef]

5. T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst. **36**(1), 129–135 (1989). [CrossRef]

1. J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging **19**, 1261–1266 (2000). [CrossRef]

6. A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens. **14**(9), 1735–1758 (1993). [CrossRef]

7. 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**(24), 2878–2880 (2004). [CrossRef]

15. K. Yung, S. Lee, and J. Schmitt, “Phase-Domain Processing of Optical Coherence Tomography Images,” J. Biomed. Opt. **4**(1), 125–136 (1999). [CrossRef]

19. M. Pircher, E. Gtzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. **8**, 565–569 (2003). [CrossRef] [PubMed]

20. 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]

21. A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. R. 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). [CrossRef] [PubMed]

23. J. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. **42**(7), 1427–1439 (1997). [CrossRef] [PubMed]

26. M. Kobayashi, H. Hanafusa, K. Takada, and J. Noda, “Polarization-Independent Interferometric Optical-Time-Domain Reflectometer,” J. Lightwave Technol. **9**, 623–628 (1991). [CrossRef]

*in-vivo*from rodent (rat) retinas, the novel algorithm shows significantly improved overall image quality and clear preservation of small structural features in the retinal images.

## 2. Theory

*S*be a set of sites on a discrete lattice 𝓛 which defines an OCT image and let

*s*∈

*S*be a site in 𝓛. Let

*M*= {

*M*(

*s*)|

*s*∈

*S*},

*A*= {

*A*(

*s*)|

*s*∈

*S*}, and

*N*= {

*N*(

*s*)|

*s*∈

*S*} be fields on

*S*. Given the measured data

*M*(

*s*) that we have acquired, let

*A*(

*s*) and

*N*(

*s*) be random variables representing noise-free data and speckle noise of unknown distribution at site

*s*respectively. Let

*m*= {

*m*(

*s*)|

*s*∈

*S*},

*a*= {

*a*(

*s*)|

*s*∈

*S*}, and

*n*= {

*n*(

*s*)|

*s*∈

*S*} be realizations of

*M*,

*A*, and

*N*respectively. Speckle in OCT imagery arises from the constructive and destructive interference of the backscattered signal from biological issues [16

16. W. Drexler, “Ultrahigh-resolution optical coherence tomography,” J. Biomed. Opt. **9**, 47–74 (2004). [CrossRef] [PubMed]

17. J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. **4**, 95–105 (1999). [CrossRef]

*a*(

*s*) from the speckle noise

*n*(

*s*) very challenging. We propose to tackle these issues associated with speckle noise reduction by estimating the noise-free data in the logarithm space using a general Bayesian least squares estimation approach based on conditional posterior sampling. The proposed speckle noise reduction algorithm can be described as follows. To handle the data-dependent nature of speckle noise, the noise-free data

*a*(

*s*) and the speckle noise

*n*(

*s*) are decoupled by projecting the measured data

*m*(

*s*) into the logarithm space,

*a*(

_{l}*s*) can be defined by the expression,

*â*(

_{l}*s*) based on the measured data

*m*(

_{l}*s*). Minimizing the expression in Eq. (3) gives,

*a*(

_{l}*s*) is essentially the statistical average based on the measured data

*m*(

_{l}*s*). The posterior distribution

*p*(

*a*(

_{l}*s*) |

*m*(

_{l}*s*)), which represents the probability distribution of the noise-free data

*â*(

_{l}*s*) based on the knowledge of the measured data

*m*(

_{l}*s*), can be highly complicated and nonlinear in nature, making it difficult to solve for

*â*(

_{l}*s*) using Eq. (4). Typically, simpler Bayesian linear least squares estimators [2] and estimators based on specific parametric posterior distribution models [6

6. A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens. **14**(9), 1735–1758 (1993). [CrossRef]

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)).

*m*(

_{l}*s*) to estimate the posterior distribution

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) based on conditions that identify the relevance of that information to accurate estimation. For example, when estimating the posterior distribution of a pixel with strong boundary characteristics, conditional posterior sampling adaptively selects information from the measured data that has similar boundary characteristics to estimate the posterior distribution, as that would allow the boundary characteristics to be preserved while averaging out the speckle noise.

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) can be described as follows. In Markov-Chain Monte Carlo density estimation [27

27. W. Hastings, “Monte carlo sampling methods using Markov chains and their applications,” Biometrika **57**(1), 97–109 (1970). [CrossRef]

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*))) is estimated in an indirect manner by first sampling from a known initial probability distribution

*Q*. Similarly, in the proposed approach, a random site

*s*′ in

*S*is determined based on an initial probability distribution

*Q*(

*s*′|

*s*).

*Q*(

*s*′|

*s*) is defined as a Gaussian distribution centered at

*s*,

*s*′-

*s*∥

^{2}denotes the squared Euclidean distance of a site

*s*′ from

*s*, and σ

_{spatial}represents the spatial variance of the initial probability distribution

*Q*(

*s*′|

*s*), which is set to 7 pixels as it was shown to provide accurate estimates during testing. Testing under different imaging conditions as well as different resolutions have shown that the use of σ

_{spatial}= 7 allows for consistently accurate results, thus making that setting suitable for a wide range of imaging scenarios. The reason for the strong performance using this setting is that the algorithm is adapts to the underlying statistics of the image and as such performs well as long as the selected area to select samples from is large enough to obtain good statistics from, irrespective of the resolution of the image. This initial probability distribution

*Q*(

*s*′|

*s*) generates sites that tend to be in closer proximity to site

*s*. Given the drawn site

*s*′, the inclusion of

*s*′ as a realization of

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) is determined based on the condition,

*μ*(

*s*) is the local mean of the neighborhood centered at

*s*and

*σ*is the estimated image noise variance. The local mean

*μ*(

*s*) is computed in a 7 × 7 region centered at

*s*in the current implementation, as that provides sufficient information to obtain good statistics from, irrespective of the resolution of the image. Furthermore, in the current implementation of the proposed method, the noise variance

*σ*was estimated by taking the local variance within a 7 × 7 region for the same reason of obtaining good statistics. Equation (6) enforces the inclusion of

*s*′ as a realization of

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) within two standard deviations, which accounts for the effect of noise variations. The inclusion of

*s*′ as a realization of

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) is based on the assumption that the local mean is a reasonable initial estimate of

*a*(

_{l}*s*). This conditional sampling is repeated until the maximum number of sites used to estimate the original signal, denoted as

*γ*, is drawn.

*γ*sites drawn from

*Q*(

*s*′|

*s*), denoted as Ω = {

*s*′

_{1},

*s*′

_{2},…,

*s*′

_{γ}}, the weight associated with each site

*s*′

_{i}in estimating

*a*(

_{l}*s*), denoted as

*w*(

*s*′

_{i}|

*s*), is computed using the following Gibbs-based likelihood function based on the local means of

*s*′

_{i}and

*s*,

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) by weighing sites with local means similar to the local mean of site

*s*higher since they are more likely to be true realizations of

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)).

*s*′

_{1},

*s*′

_{2},…,

*s*′

_{γ}} and the associated set of weights

*W*= {

*w*(

*s*′

_{1},

*s*),

*w*(

*s*′

_{2},

*s*),…,

*w*(

*s*′

_{γ},

*s*)}, the posterior distribution

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) is then estimated using a weighted histogram approach. Suppose that the discrete range of possible measured data values (

*m*) be [

_{l}*L*

_{min},

*L*

_{max}], where

*L*

_{min}and

*L*

_{max}are the minimum and maximum possible values, respectively. Let the discrete range of possible noise-free data values (

*a*) also be [

_{l}*L*

_{min},

*L*

_{max}]. Furthermore, let

*h*(

*r*) be a weighted histogram, defined over [

_{k}*L*

_{min},

*L*

_{max}], where

*r*is the

_{k}*k*

^{th}possible noise-free data value. For each site

*s*′

_{i}, the weight

*w*(

*s*′

_{i}|

*s*) is accumulated in the histogram bin of the weighted histogram that corresponds to

*m*(

_{l}*s*′

_{i}) (i.e.,

*h*(

*r*=

_{k}*m*(

_{l}*s*′

_{i}))). After constructing the weighted histogram, each histogram bin is then divided by the sum of all weights to construct a normalized histogram representing

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)). Therefore,

*p̂*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) can be formulated as

*δ*(․) is the Dirac delta function and Z is a normalization term such that

*a*(

*s*) can be found by back-projecting the Bayesian estimate of

*a*(

_{l}*s*) computed using the estimated

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)) [Eq. (4)] from the logarithm space using

*â*(

*s*) = exp[

*â*(

_{l}*s*)]. A flowchart detailing a step-by-step breakdown of the proposed despeckling algorithm is shown in Fig. 1.

## 3. Results and discussion

*in-vivo*with a research grade UHROCT system. Two representative images acquired at and away from the optic disc of the rat eye (Fig. 2) are discussed.

28. P. Puvanathasan, P. Forbes, Z. Ren, D. Malchow, S. Boyd, and K. Bizheva, “High-speed, high-resolution Fourier-domain optical coherence tomography system for retinal imaging in the 1060 nm wavelength region,” Opt. Lett. **33**(21), 2479–2481 (2008). [PubMed]

*P*= 10mW) and data is acquired with a 47kHz data rate, InGaAs linear array, 1024 pixel camera (SUI, Goodrich) interfaced with a high performance spectrometer (P&P Optica). The UHROCT system provides 3

_{out}*μm*axial and 5

*μm*lateral resolution in retinal tissue and 100dB SNR for 1.3mW optical power incident on the rat cornea.

*μ*m in diameter) in the inner- and outer plexiform layers (red arrows) and the larger choroidal blood vessels (yellow arrows). For comparison purposes, the same images were also processed with some of the high performance wavelet- or diffusion-based image processing algorithms, such as the adaptive median filter proposed by Loupas et.al. [5

5. T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst. **36**(1), 129–135 (1989). [CrossRef]

3. 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 Anal. Machine Intell. **4**(2), 157–166 (1982). [CrossRef]

9. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. **22**(3), 323–331 (2003). [CrossRef]

6. A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens. **14**(9), 1735–1758 (1993). [CrossRef]

12. Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. **11**(11), 1260–1270 (2002). [CrossRef]

13. P. Puvanathasan and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images,” Opt. Express **17**(2), 733–746 (2009). [CrossRef] [PubMed]

*γ*, was set to 64 and 7 + 7 neighborhoods were used, as they were proven to produce good estimates of

*p*(

*a*(

_{l}*s*)|

*m*(

_{l}*s*)). Tests performed under different imaging conditions, resolutions, as well as biological tissues other than retina (e.g., corneal and skin) and the testing have shown that selection of these parameters work well for a wide variety of different imaging scenarios. The reason for the strong performance using such parameters is that the proposed algorithm adapts to the underlying statistics of the image and as such performs well as long as the selected area to select samples from is large enough to obtain good statistics from, irrespective of the resolution of the image. As such, the use of the presented parameters should provide strong speckle noise reduction performance for most practical situations. All algorithms were implemented in MATLAB and tested on an Intel Pentium 4, 3 GHz machine with 1 GB of RAM. For direct comparison of the algorithms performance, 3 regions of the retinal image [Fig. 2(b)] were selected (red-line boxes), focusing on specific morphological features such as the retinal capillaries and surface blood vessels (box #1), the boundaries between retinal layers (box #2), the choroidal blood vessels (box #3). Significant efforts were made to ensure fair comparisons between the proposed algorithm and other tested speckle denoising algorithms. Extensive parametric testing showed that the parameters proposed in the associated research literature for the tested algorithms provide the strongest results that can be obtained using these methods for the tested retinal images, as changes to these parameters yield no improvements in terms of the results.

*η*) over the

*in-vivo*OCT retinal images were computed for each tested method. The image quality metrics used are the same as the metrics used in [7

7. 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**(24), 2878–2880 (2004). [CrossRef]

10. P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express **15**(24), 15747–15758 (2007). [CrossRef] [PubMed]

13. P. Puvanathasan and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images,” Opt. Express **17**(2), 733–746 (2009). [CrossRef] [PubMed]

*A*and

*σ*

^{2}represent the linear magnitude image and the variance of the background noise region in the linear magnitude image respectively. In the expression for ENL,

*μ*and

_{h}*σ*

_{h}^{2}represent the mean and the variance of the

*h*

^{th}homogenous region of interests respectively. In the definition for CNR,

*μ*and

_{b}*σ*

_{b}^{2}represent the mean and the variance of the same background noise region as in SNR and

*μ*and

_{r}*σ*

_{r}^{2}represent the mean and the variance of the

*r*

^{th}region of interest which includes the homogeneous regions. In the edge preservation measure, ∇

^{2}

*M*and ∇

^{2}

*A*represent the Laplacian operator performed on the original image and the filtered image respectively. Also,

^{2}

*M*and ∇

^{2}

*A*respectively.

9. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. **22**(3), 323–331 (2003). [CrossRef]

**14**(9), 1735–1758 (1993). [CrossRef]

12. Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. **11**(11), 1260–1270 (2002). [CrossRef]

*O*(

*n*), and as such the computational speed scales linear with the number of pixels in the image, which is a desirable property especially when dealing with high resolution imagery.

## 4. Conclusion

*in-vivo*retinal images show that the proposed method results in SNR improvements of over 17 dB and 2 dB compared to the next best tested method. The proposed method shows great potential in not only improving the overall visual appearance of retinal OCT images, but also the accuracy of image segmentation, registration, and other post-processing algorithms for analyzing OCT tomograms. Furthermore, the novel algorithm has the advantages of providing high contrast and very sharp appearance of zoomed-in sections of the original image, minimal presence of image artefacts, as well as resulting in best performance regardless of the type of image features layered or irregularly shaped structures in the original image. When combined with segmentation algorithms designed for retinal layers or blood vessels, the proposed algorithm can result in improved precision of the quantitative evaluation of individual retinal layer thickness, or measurement of the blood vessel diameter, a parameter necessary for the precise evaluation of retinal and choroidal blood flow.

## Acknowledgment

## References and links

1. | J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging,” IEEE Trans. Med. Imaging |

2. | J. Lee, “Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng. |

3. | 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 Anal. Machine Intell. |

4. | D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. |

5. | T. Loupas, W. Mcdicken, and P. Allen, “An adaptive weighted median filter for speckle suppression in medical ultrasound images,” IEEE Trans. Circuits Syst. |

6. | A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and adaptive speckle filtering in SAR images,” Int. J. Remote Sens. |

7. | 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. |

8. | A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A |

9. | A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. |

10. | P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express |

11. | S. Aja, C. Alberola, and J. Ruiz, “Fuzzy anisotropic diffusion for speckle filtering,” Proc. IEEE ICASSP |

12. | Y. Yu and S. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. |

13. | P. Puvanathasan and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images,” Opt. Express |

14. | D. Fernandez, H. Salinas, and C. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express |

15. | K. Yung, S. Lee, and J. Schmitt, “Phase-Domain Processing of Optical Coherence Tomography Images,” J. Biomed. Opt. |

16. | W. Drexler, “Ultrahigh-resolution optical coherence tomography,” J. Biomed. Opt. |

17. | J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. |

18. | J. Kim, D. Miller, E. Kim, S. Oh, J. Oh, and T. Milner, ”Optical coherence tomography speckle reduction by a partially spatially coherent source,” J. Biomed. Opt. |

19. | M. Pircher, E. Gtzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. |

20. | N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by path length encoded angular compounding,” J. Biomed. Opt. |

21. | A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. R. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction,” Opt. Express |

22. | T. Jorgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in Proc. SPIE 6627, Munich, Germany 66270P (2007). |

23. | J. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. |

24. | M. Bashkansky and J. Reintjes, “Statistics and reduction of speckle in optical coherence tomography,” Opt. Lett. |

25. | D. Popescu, M. Hewkoa, and M. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Comm. |

26. | M. Kobayashi, H. Hanafusa, K. Takada, and J. Noda, “Polarization-Independent Interferometric Optical-Time-Domain Reflectometer,” J. Lightwave Technol. |

27. | W. Hastings, “Monte carlo sampling methods using Markov chains and their applications,” Biometrika |

28. | P. Puvanathasan, P. Forbes, Z. Ren, D. Malchow, S. Boyd, and K. Bizheva, “High-speed, high-resolution Fourier-domain optical coherence tomography system for retinal imaging in the 1060 nm wavelength region,” Opt. Lett. |

**OCIS Codes**

(030.6140) Coherence and statistical optics : Speckle

(100.0100) Image processing : Image processing

(100.2980) Image processing : Image enhancement

(170.4500) Medical optics and biotechnology : Optical coherence tomography

(100.3008) Image processing : Image recognition, algorithms and filters

**ToC Category:**

Medical Optics and Biotechnology

**History**

Original Manuscript: January 21, 2010

Revised Manuscript: March 24, 2010

Manuscript Accepted: March 30, 2010

Published: April 6, 2010

**Virtual Issues**

Vol. 5, Iss. 8 *Virtual Journal for Biomedical Optics*

**Citation**

Alexander Wong, Akshaya Mishra, Kostadinka Bizheva, and David A. Clausi, "General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery," Opt. Express **18**, 8338-8352 (2010)

http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-18-8-8338

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### References

- J. Rogowska and M. E. Brezinski, "Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging," IEEE Trans. Med. Imaging 19, 1261-1266 (2000). [CrossRef]
- J. Lee, "Speckle suppression and analysis for synthetic aperture radar," Opt. Eng. 25(5), 636-643 (1986).
- 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 Anal. Machine Intell. 4(2), 157-166 (1982). [CrossRef]
- D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, "Adaptive restoration of images with speckle," IEEE Trans. Acoust. Speech Signal Process. 35(3), 373-383 (1987). [CrossRef]
- T. Loupas, W. Mcdicken, and P. Allen, "An adaptive weighted median filter for speckle suppression in medical ultrasound images," IEEE Trans. Circuits Syst. 36(1), 129-135 (1989). [CrossRef]
- A. Lopes, E. Nezry, R. Touzi, and H. Laur, "Structure detection and adaptive speckle filtering in SAR images," Int. J. Remote Sens. 14(9), 1735-1758 (1993). [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(24), 2878-2880 (2004). [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," J. Opt. Soc. Am. A 24(7), 1901-1910 (2007). [CrossRef]
- A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, "A versatile wavelet domain noise filtration technique for medical imaging," IEEE Trans. Med. Imag. 22(3), 323-331 (2003). [CrossRef]
- P. Puvanathasan and K. Bizheva, "Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set," Opt. Express 15(24), 15747-15758 (2007). [CrossRef] [PubMed]
- S. Aja, C. Alberola, and J. Ruiz, "Fuzzy anisotropic diffusion for speckle filtering," Proc. IEEE ICASSP 2, 1261-1264 (2001).
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