Self-fusion for OCT noise reduction

Ipek Oguz; Joseph D. Malone; Yigit Atay; Yuankai K. Tao

doi:10.1117/12.2549472

10 March 2020 Self-fusion for OCT noise reduction

Ipek Oguz, Joseph D. Malone, Yigit Atay, Yuankai K. Tao

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113130C (2020) https://doi.org/10.1117/12.2549472
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Ipek Oguz, Joseph D. Malone, Yigit Atay, and Yuankai K. Tao "Self-fusion for OCT noise reduction", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130C (10 March 2020); https://doi.org/10.1117/12.2549472

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