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Speckle Noise Reduction in Optical Coherence Tomography Using Two-Dimensional Curvelet-Based Dictionary Learning Publisher



Esmaeili M1 ; Dehnavi AM1 ; Rabbani H1 ; Hajizadeh F2
Authors
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Authors Affiliations
  1. 1. Department of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Noor Ophthalmology Research Center, Noor Eye Hospital, Tehran, Iran

Source: Journal of Medical Signals and Sensors Published:2017


Abstract

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained. © 2017 Journal of Medical Signals & Sensors.
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