Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.
Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.
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DOI：
10.1155/2013/417491
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发表时间：
2013
影响因子：
7.6
通讯作者：
Abramoff MD
中科院分区：
文献类型：
--
作者：
Rabbani H;Sonka M;Abramoff MD
本文将最小均方误差估计器用于三维复数小波域无噪声三维OCT数据恢复。由于所提出的无噪声数据分布对MMSE估计器的性能起着关键作用，因此提出了一种能够模拟小波主要统计特性的无噪声三维复小波系数pdf的先验分布。我们使用两个带局部参数的二元高斯pdf的混合来模拟系数，这些参数能够捕捉系数的重尾特性以及级间和级内相关性。另外，针对OCT图像的特殊结构，采用各向异性加窗方法进行局部参数估计，提高了图像的视觉质量。在此基础上，利用高斯/双边瑞利噪声分布和同态/非同态模型，得到了几种OCT相干斑抑制算法。为了评估算法的性能，我们使用了650×512×128个OCT数据集中的156个感兴趣区，在湿性AMD病理情况下进行了检测。我们的模拟结果表明，对于存在高斯噪声的非同态模型，基于局部二元混合先验的最小均方误差估计器是最好的，从而使信噪比提高了7.8±1.7。
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.