ArticleScientific reports2018
Quantitative evaluation of comb-structure correction methods for multispectral fibrescopic imaging.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis.Neurophotonics · 2024Article
- Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.Journal of biomedical optics · 2022Article
- Effects of Image Processing Using Honeycomb-Removal and Image-Sharpening Algorithms on Visibility of 27-Gauge Endoscopic Vitrectomy.Journal of clinical medicine · 2022Article
- Improvement in Dacryoendoscopic Visibility after Image Processing Using Comb-Removal and Image-Sharpening Algorithms.Journal of clinical medicine · 2022Article
- First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia.Journal of biomedical optics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Removing the comb artifact introduced by imaging fibre bundles, or 'fibrescopes', for example in medical endoscopy, is essential to provide high quality images to the observer. Multispectral imaging (MSI) is an emerging method that combines morphological (spatial) and chemical (spectral) information in a single data 'cube'. When a fibrescope is coupled to a spectrally resolved detector array (SRDA) to perform MSI, comb removal is complicated by the demosaicking step required to reconstruct the multispectral data cube. To understand the potential for using SRDAs as multispectral imaging sensors in medical endoscopy, we assessed five comb correction methods with respect to five performance metrics relevant to biomedical imaging applications: processing time, resolution, smoothness, signal and the accuracy of spectral reconstruction. By assigning weights to each metric, which are determined by the particular imaging application, our results can be used to select the correction method to achieve best overall performance. In most cases, interpolation gave the best compromise between the different performance metrics when imaging using an SRDA.
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Registered trials
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