Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15948
Title: A cascaded convolutional neural network architecture for despeckling OCT images
Authors: Anoop B.N.
Kalmady K.S.
Udathu A.
Siddharth V.
Girish G.N.
Kothari A.R.
Rajan J.
Issue Date: 2021
Citation: Biomedical Signal Processing and Control , Vol. 66 , , p. -
Abstract: Optical Coherence Tomography (OCT) is an imaging technique widely used for medical imaging. Noise in an OCT image generally degrades its quality, thereby obscuring clinical features and making the automated segmentation task suboptimal. Obtaining higher quality images requires sophisticated equipment and technology, available only in selected research settings, and is expensive to acquire. Developing effective denoising methods to improve the quality of the images acquired on systems currently in use has potential for vastly improving image quality and automated quantitative analysis. Noise characteristics in images acquired from machines of different makes and models may vary. Our experiments show that any single state-of-the-art method for noise reduction fails to perform equally well on images from various sources. Therefore, detailed analysis is required to determine the exact noise type in images acquired using different OCT machines. In this work we studied noise characteristics in the publicly available DUKE and OPTIMA datasets to build a more efficient model for noise reduction. These datasets have OCT images acquired using machines of different manufacturers. We further propose a patch-wise training methodology to build a system to effectively denoise OCT images. We have performed an extensive range of experiments to show that the proposed method performs superior to other state-of-the-art-methods. © 2021 Elsevier Ltd
URI: https://doi.org/10.1016/j.bspc.2021.102463
http://idr.nitk.ac.in/jspui/handle/123456789/15948
Appears in Collections:1. Journal Articles

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