Evidence map›Paper›PMID 36827787›Full record

ArticleComputers in biology and medicine2023

Mixed multiscale BM4D for three-dimensional optical coherence tomography denoising.

Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani, Bhavna Josephine Antony, Hiroshi Ishikawa

Abstract read
In one paragraph

Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ashkan AbbasiDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, USA.
Amirhassan MonadjemiSchool of Continuing and Lifelong Education, National University of Singapore, Singapore.
Leyuan FangCollege of Electrical and Information Engineering, Hunan University, China.
Hossein RabbaniDepartment of Biomedical Engineering, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran.
Bhavna Josephine AntonyElectrical and Computer System Engineering, Faculty of Engineering, Monash University, Australia; Department of Infectious Diseases, Alfred Health, Australia.
Hiroshi IshikawaDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, USA. Electronic address: ishikawh@ohsu.edu.

Funding

Deep Learning Approaches for Personalized Modeling and Forecasting of Glaucomatous ChangesR01EY030929 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI ISHIKAWA, HIROSHI · 2020 to 2024
$1.9M
NEI NIH HHS R01 EY030929
6 · The paper itself

Abstract

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated.

Indexed as

RetinaTomography, Optical CoherenceAlgorithmsData CollectionImage Processing, Computer-AssistedSignal-To-Noise RatioBM4DMultiscale denoisingOptical coherence tomographySparse representationsWavelets

Identifiers

PMID36827787
PMCPMC10739784

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.