ArticleNeuroImage2022
A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography.Light, science & applications · 2025Article
- Probabilistic volumetric speckle suppression in OCT using deep learning.Biomedical optics express · 2024Article
- Enhanced Multiscale Human Brain Imaging by Semi-supervised Digital Staining and Serial Sectioning Optical Coherence Tomography.Research square · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
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Abstract
Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.
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Registered trials
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