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Optical coherence tomography noise reduction using anisotropic local bivariate gaussian mixture prior in 3D complex wavelet domain
Journal article   Open access   Peer reviewed

Optical coherence tomography noise reduction using anisotropic local bivariate gaussian mixture prior in 3D complex wavelet domain

Hossein Rabbani, Milan Sonka and Michael D Abramoff
International Journal of Biomedical Imaging, Vol.2013, 417491
2013
DOI: 10.1155/2013/417491
PMCID: PMC3810483
PMID: 24222760
url
https://doi.org/10.1155/2013/417491View
Published (Version of record) Open Access

Abstract

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

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