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Proceedings Paper

Self-fusion for OCT noise reduction
Author(s): Ipek Oguz; Joseph D. Malone; Yigit Atay; Yuankai K. Tao
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Paper Abstract

Reducing speckle noise is an important task for improving visual and automated assessment of retinal OCT images. Traditional image/signal processing methods only offer moderate speckle reduction; deep learning methods can be more effective but require substantial training data, which may not be readily available. We present a novel self-fusion method that offers effective speckle reduction comparable to deep learning methods, but without any external training data. We present qualitative and quantitative results in a variety of datasets from fovea and optic nerve head regions, with varying SNR values for input images.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130C (10 March 2020); doi: 10.1117/12.2549472
Show Author Affiliations
Ipek Oguz, Vanderbilt Univ. (United States)
Joseph D. Malone, Vanderbilt Univ. (United States)
Yigit Atay, Vanderbilt Univ. (United States)
Yuankai K. Tao, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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