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X-Let's Atom Combinations for Modeling and Denoising of Oct Images by Modified Morphological Component Analysis Publisher Pubmed



Razavi R1 ; Plonka G1 ; Rabbani H2
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Authors Affiliations
  1. 1. Institute for Numerical and Applied Mathematics, University of Gottingen, Gottingen, 37083, Germany
  2. 2. Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan, 8174673461, Iran

Source: IEEE Transactions on Medical Imaging Published:2024


Abstract

An improved analysis of Optical Coherence Tomography (OCT) images of the retina is of essential importance for the correct diagnosis of retinal abnormalities. Unfortunately, OCT images suffer from noise arising from different sources. In particular, speckle noise caused by the scattering of light waves strongly degrades the quality of OCT image acquisitions. In this paper, we employ a Modified Morphological Component Analysis (MMCA) to provide a new method that separates the image into components that contain different features as texture, piecewise smooth parts, and singularities along curves. Each image component is computed as a sparse representation in a suitable dictionary. To create these dictionaries, we use non-data-adaptive multi-scale ( X -let) transforms which have been shown to be well suitable to extract the special OCT image features. In this way, we reach two goals at once. On the one hand, we achieve strongly improved denoising results by applying adaptive local thresholding techniques separately to each image component. The denoising performance outperforms other state-of-the-art denoising algorithms regarding the PSNR as well as no-reference image quality assessments. On the other hand, we obtain a decomposition of the OCT images in well-interpretable image components that can be exploited for further image processing tasks, such as classification. © 1982-2012 IEEE.
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