Quantitative evaluation of comb-structure correction methods for multispectral fibrescopic imaging

Waterhouse, Dale J.; Luthman, Anna Siri; Yoon, Jonghee; Gordon, George S. D.; Bohndiek, Sarah E.

doi:10.1038/s41598-018-36088-7

Cited by 8 publications

(8 citation statements)

References 44 publications

(41 reference statements)

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“…We have previously shown that SRDAs can be implemented in combination with such an imaging-fiber bundle without reducing resolution, since the resolution of fiberscope-based imaging is limited by the size of individual fiberlets rather than by the sensor resolution. 45 The babyscope system included: a single fiber to relay illumination from outside the patient and direct it onto the tissue; and a 10,000-fiberlet imaging bundle to relay diffusely reflected light from the esophageal tissue to custom external detection optics [ Fig. 1(a) ].…”

Section: Methodsmentioning

confidence: 99%

“…2(a) ]. For analysis, these were removed in a process of demosaicing and decombing as described previously, 45 resulting in a

band image cube [ Fig. 2(b) ] with a spatial resolution of

at a working distance of 1 cm.…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, raw images were checked for saturation (

), dark subtracted then decombed, to account for the structure introduced to the image by the individual fiberlets, and demosaiced, to separate the nine spectral bands, creating an image cube as outlined previously. 45 In the image cube, low signal pixels were removed (max per-pixel

) as they are more likely to be affected by noise. Finally, each per-pixel spectrum was divided by a white light reference spectrum and normalized to the maximum of the per-pixel spectrum, such that the per-pixel spectra represent max-normalized reflectance.…”

Section: Methodsmentioning

confidence: 99%

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First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett’s-related neoplasia

et al. 2021

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. Significance: The early detection of dysplasia in patients with Barrett’s esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy. Aim: We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract. Approach: We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, -nearest neighbor classification, and a neural network. Results: Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and -nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett’s esophagus and neoplasia based on majority decisions per-image. Conclusions: MSI shows promise for disease classification in Barrett’s esophagus and merits further investigation as a tool in high-definition “chip-on-tip” endoscopes.

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Section: Methodsmentioning

confidence: 99%

“…2(a) ]. For analysis, these were removed in a process of demosaicing and decombing as described previously, 45 resulting in a

band image cube [ Fig. 2(b) ] with a spatial resolution of

at a working distance of 1 cm.…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, raw images were checked for saturation (

Section: Methodsmentioning

confidence: 99%

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First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett’s-related neoplasia

et al. 2021

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“…AUC normalization, as used by Lu et al, 32,33 Kumashiro et al, 34 Ma et all., 35 Waterhouse et al, 36 and Leitner et al, 37…”

Section: Area Under the Curvementioning

confidence: 99%

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

et al. 2022

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Significance: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.Aim: To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.Approach: We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.Conclusions: Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

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“…This is especially true with smaller-gauge systems; the cross-section area of a 27-gauge probe is only 20% of that of a 20-gauge probe. Furthermore, images obtained with fiberscope-type endoscopes contain black fine mesh noise (dark honeycomb-like artifacts), which originates from the opaque cladding layer surrounding each single fiber in the image conductor [ 5 , 6 , 7 ]. Such artifacts adversely affect visibility during endoscopic procedures.…”

Section: Introductionmentioning

confidence: 99%

Effects of Image Processing Using Honeycomb-Removal and Image-Sharpening Algorithms on Visibility of 27-Gauge Endoscopic Vitrectomy

Tasaki

Nishimura

Hida

et al. 2022

JCM

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Endoscopic vitrectomy with small gauge probes has clinical potentials, but intraocular visibility is inherently limited by low resolution and dim illumination due to the reduced number of optic fibers. We investigated whether honeycomb-removal and image-sharpening algorithms, which enable real-time processing of live images with a delay of 0.004 s, can improve the visibility of 27-gauge endoscopic vitrectomy. A total of 33 images during endoscopic vitrectomy were prepared, consisting of 11 original images, 11 images after the honeycomb-removal process, and 11 images after both honeycomb-removal and image-sharpening procedures. They were randomly presented to 18 vitreous surgeons, who rated each image on a 10-point scale. The honeycomb-removal algorithm almost completely suppressed honeycomb artifacts without degrading the background image quality. The implementation of image-sharpening algorithms further improved endoscopic visibility by optimizing contrast and augmenting image clarity. The visibility score was significantly improved from 4.27 ± 1.78 for the original images to 4.72 ± 2.00 for the images after the honeycomb-removal process (p < 0.001, linear mixed effects model), and to 5.40 ± 2.10 for the images after both the honeycomb-removal and image-sharpening procedures (p < 0.001). When the visibility scores were analyzed separately for 10 surgeons who were familiar with endoscopic vitrectomy and 8 surgeons who were not, similar results were obtained. Image processing with honeycomb-removal and image-sharpening algorithms significantly improved the visibility of 27-gauge endoscopic vitrectomy.

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Quantitative evaluation of comb-structure correction methods for multispectral fibrescopic imaging

Cited by 8 publications

References 44 publications

First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett’s-related neoplasia

First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett’s-related neoplasia

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Effects of Image Processing Using Honeycomb-Removal and Image-Sharpening Algorithms on Visibility of 27-Gauge Endoscopic Vitrectomy

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