%0 Journal Article
%T Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain
%A Hossein Rabbani
%A Milan Sonka
%A Michael D. Abramoff
%J International Journal of Biomedical Imaging
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/417491
%X 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 ¡Á 512 ¡Á 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 ¡À 1.7 in CNR. 1. Introduction Optical coherence tomography (OCT) is an optical signal acquisition and processing method that captures 3D images from within optical scattering media such as biological tissues [1¨C4]. For example, in ophthalmology, OCT is used to obtain detailed images from within the retina [4]. Similar to other optical tomographic techniques, OCT suffers from speckle noise that reduces the ability of image interpretation [5]. So, noise reduction is an essential part of OCT image processing systems. Until now, several techniques for OCT noise reduction have been reported [6¨C14]. Initial methods perform in complex domain [15], that is, before producing magnitude of OCT interference signal, while most introduced despeckling methods are applied after an OCT image is formed [6¨C14]. These methods, which usually suppose multiplicative noise for speckled data, also can be categorized into image domain and transform domain methods. As an example for image domain techniques, in [16] the rotating kernel transform (RKT) filters are applied on an image with a set of oriented kernels and keep the largest filter output for each pixel. Other image domain methods based on enhanced Lee filter [17], median filter [17], symmetric nearest neighbor filter [17], and adaptive Wiener filter [17], and I-divergence
%U http://www.hindawi.com/journals/ijbi/2013/417491/