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• W4377013567 abstract "Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor train (TT)-and low multilinear (ML) rank approximations of 3D tensors for compression and de-speckling of 3D OCT images. Due to inherent denoising mechanism of low-rank approximation, compressed image is often even of better quality than the raw image it is based on.Approach. We formulate CR-constrained low rank approximations of 3D tensor as parallel non-convex non-smooth optimization problems implemented by alternating direction method of multipliers of unfolded tensors. In contrast to patch- and sparsity-based OCT image compression methods, proposed approach does not require clean images for dictionary learning, enables CR as high as 60:1, and it is fast. In contrast to deep networks based OCT image compression, proposed approach is training free and does not require any supervised data pre-processing.Main results. Proposed methodology is evaluated on twenty four images of a retina acquired on Topcon 3D OCT-1000 scanner, and twenty images of a retina acquired on Big Vision BV1000 3D OCT scanner. For the first dataset, statistical significance analysis shows that for CR ≤ 35, all low ML rank approximations and Schatten-0 (S0) norm constrained low TT rank approximation can be useful for machine learning-based diagnostics by using segmented retina layers. Also for CR ≤ 35,S0-constrained ML rank approximation andS0-constrained low TT rank approximation can be useful for visual inspection-based diagnostics. For the second dataset, statistical significance analysis shows that for CR ≤ 60 all low ML rank approximations as well asS0andS1/2low TT ranks approximations can be useful for machine learning-based diagnostics by using segmented retina layers. Also, for CR ≤ 60, low ML rank approximations constrained withSp,p∊ {0, 1/2, 2/3} and one surrogate ofS0can be useful for visual inspection-based diagnostics. That is also true for low TT rank approximations constrained withSp,p∊ {0, 1/2, 2/3} for CR ≤ 20.Significance. Studies conducted on datasets acquired by two different types of scanners confirmed capabilities of proposed framework that, for a wide range of CRs, yields de-speckled 3D OCT images suitable for clinical data archiving and remote consultation, for visual inspection-based diagnosis and for machine learning-based diagnosis by using segmented retina layers." @default.
• W4377013567 created "2023-05-19" @default.
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• W4377013567 date "2023-06-08" @default.
• W4377013567 modified "2023-10-16" @default.
• W4377013567 title "Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images" @default.
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• W4377013567 doi "https://doi.org/10.1088/1361-6560/acd6d1" @default.
• W4377013567 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37201537" @default.
• W4377013567 hasPublicationYear "2023" @default.
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