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Mixed Multiscale Bm4d for Three-Dimensional Optical Coherence Tomography Denoising Publisher Pubmed



Abbasi A1 ; Monadjemi A2 ; Fang L3 ; Rabbani H4 ; Antony BJ5, 6 ; Ishikawa H1, 7
Authors
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Authors Affiliations
  1. 1. Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, United States
  2. 2. School of Continuing and Lifelong Education, National University of Singapore, Singapore
  3. 3. College of Electrical and Information Engineering, Hunan University, China
  4. 4. Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
  5. 5. Electrical and Computer System Engineering, Faculty of Engineering, Monash University, Australia
  6. 6. Department of Infectious Diseases, Alfred Health, Australia
  7. 7. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, United States

Source: Computers in Biology and Medicine Published:2023


Abstract

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated. © 2023
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