Abstract
Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.
Highlights
Retinal Optical Coherence Tomography (OCT) is a recently developed imaging technique, which provides cross-sectional high-resolution images from the retinal microstructures and a non-invasive 3D view of the layered structure of the retina [1]
Numerous more advanced methods have been proposed for speckle noise reduction, such as anisotropic diffusion-based techniques [12,13,14], wavelet-based methods [15], denoising using dual-tree complex wavelet transform [16] and curvelet transform [17], sparsity-based denoising [18,19], complex wavelet-based K-times singular value decomposition (K-SVD) dictionary learning technique (CWDL) [5], deep convolutional neural network based methods [20,21,22] and robust principal component analysis (RPCA)-based method [23]
We take the 2D curvelet transform of the noisy image, each coefficient matrix is despeckled based on the 2D curvelet-based K-SVD dictionary learning
Summary
Retinal Optical Coherence Tomography (OCT) is a recently developed imaging technique, which provides cross-sectional high-resolution images from the retinal microstructures and a non-invasive 3D view of the layered structure of the retina [1]. This allows precise monitoring of diseases like Age-related Macular Degeneration (AMD) and retinopathy [2]. Many methods have been proposed over the years to address speckle noise reduction from the OCT images. Numerous more advanced methods have been proposed for speckle noise reduction, such as anisotropic diffusion-based techniques [12,13,14], wavelet-based methods [15], denoising using dual-tree complex wavelet transform [16] and curvelet transform [17], sparsity-based denoising [18,19], complex wavelet-based K-SVD dictionary learning technique (CWDL) [5], deep convolutional neural network based methods [20,21,22] and robust principal component analysis (RPCA)-based method [23]
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