Vessel contrast enhancement in hyperspectral images

Bjørgan, Asgeir; Denstedt, Martin; Milanič, Matija; Paluchowski, Lukasz A.; Randeberg, Lise Lyngsnes

doi:10.1117/12.2075703

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…One of the main advantages of this technique is that it uses non-ionizing light in a non-contact way, resulting in a non-invasive technology. For this reason, HSI is an emerging technique in the medical field and it has been researched in many different applications, such as oximetry of the retina [6][7][8], intestinal ischemia identification [9], histopathological tissue analysis [10][11][12][13], blood vessel visualization enhancement [14,15], estimation of the cholesterol levels [16], chronic cholecystitis detection [17], diabetic foot [18], etc. In particular, HSI has started to achieve promising results in the recent years with respect to cancer detection through the utilization of cutting-edge machine-learning algorithms [4,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440. 5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

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Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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“…Vessel contrast enhancement is useful for vascular visualization and segmentation . We utilized the spectral information from the hyperspectral retinal image and composed a false‐color image with enhanced vessel‐tissue color contrast.…”

Section: Resultsmentioning

confidence: 99%

Snapshot hyperspectral retinal imaging using compact spectral resolving detector array

Liu

Dong

et al. 2016

Journal of Biophotonics

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Hyperspectral retinal imaging captures the light spectrum from each imaging pixel. It provides spectrally encoded retinal physiological and morphological information, which could potentially benefit diagnosis and therapeutic monitoring of retinal diseases. The key challenges in hyperspectral retinal imaging are how to achieve snapshot imaging to avoid motions between the images from multiple spectral bands, and how to design a compact snapshot imager suitable for clinical use. Here, we developed a compact, snapshot hyperspectral fundus camera for rodents using a novel spectral resolving detector array (SRDA), on which a thin-film Fabry–Perot cavity filter was monolithically fabricated on each imaging pixel. We achieved hyperspectral retinal imaging with 16 wavelength bands (460 to 630 nm) at 20 fps. We also demonstrated false-color vessel contrast enhancement and retinal oxygen saturation (sO2) measurement through spectral analysis. This work could potentially bring hyperspectral retinal imaging from bench to bedside.

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“…Medical hyperspectral imaging has been employed in several different areas such as blood vessel visualization enhancement [36] , estimation of cholesterol levels [37] , histopathological tissue analysis [38,39] , and identi cation of glioblastoma [40] . There are no studies regarding membranous nephropathy nor the distinction between IMN and HBV-MN.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

Wei

Yang

et al. 2020

Preprint

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Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue. Methods We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.

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Vessel contrast enhancement in hyperspectral images

Cited by 13 publications

References 18 publications

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Snapshot hyperspectral retinal imaging using compact spectral resolving detector array

Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

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