Cooperative low-rank models for removing stripe noise from OCTA images

Wu, Xiyin, Gao, Dongxu ORCID: 0000-0001-7008-0737, Borroni, Davide ORCID: 0000-0001-6952-5647, Madhusudhan, Savita, Jin, Zhong and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2020) Cooperative low-rank models for removing stripe noise from OCTA images. IEEE Journal of Biomedical and Health Informatics, 24 (12). pp. 3480-3490.

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Official URL: https://ieeexplore.ieee.org/document/9099639

Abstract

Optical coherence tomography angiography (OCTA) is an emerging non-invasive imaging technique for imaging the microvasculature of the eye based on phase variance or amplitude decorrelation derived from repeated OCT images of the same tissue area. Stripe noise occurs during the OCTA acquisition process due to the involuntary movement of the eye. To remove the stripe noise (or ‘destriping’) effectively, we propose two novel image decomposition models to simultaneously destripe all the OCTA images of the same eye cooperatively: cooperative uniformity destriping (CUD) model and cooperative similarity destriping (CSD) model. Both the models consider stripe noise by low-rank constraint but in different ways: the CUD model assumes that stripe noise is identical across all the layers while the CSD model assumes that the stripe noise at different layers are different and have to be considered in the model. Compared to the CUD model, CSD is a more general solution for real OCTA images. An efficient solution (CSD+) is developed for model CSD to reduce the computational complexity. The models were extensively evaluated against state-of-the-art methods on both synthesized and real OCTA datasets. The experiments demonstrated not only the effectiveness of the CSD and CSD+ models in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) and CSD+ is twice faster than CSD, but also their beneficiary effect on the vessel segmentation of OCTA images. We expect our models will become a powerful tool for clinical applications.

Item Type:	Article
Uncontrolled Keywords:	Computational modeling, Retina, Optimization, Biomedical imaging, Mathematical model, Machine learning, OCTA destriping, cooperative, retina vessel segmentation, low-rank, total variation
Depositing User:	Symplectic Admin
Date Deposited:	15 Jan 2021 09:19
Last Modified:	18 Jan 2023 23:03
DOI:	10.1109/jbhi.2020.2997381
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3113622