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   speckle noise reduction in optical coherence tomography using twodimensional curvelet-based dictionary learning  
   
نویسنده esmaeili mahdad ,mehri dehnavi alireza ,rabbani hossein ,hajizadeh fedra
منبع journal of medical signals and sensors - 2017 - دوره : 7 - شماره : 2 - صفحه:86 -91
چکیده    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.
کلیدواژه curvelet transform ,dictionary learning ,optical coherence tomography ,speckle noise
آدرس isfahan university of medical sciences, department of advanced medical technology, iran, isfahan university of medical sciences, department of advanced medical technology, iran, isfahan university of medical sciences, department of advanced medical technology, iran, noor eye hospital, noor ophthalmology research center, iran
پست الکترونیکی fhajizadeh@noorvision.com
 
     
   
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