Hyperspectral Imaging for Clinical Applications

Yoon, Jonghee

doi:10.1007/s13206-021-00041-0

Cited by 77 publications

(36 citation statements)

References 98 publications

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“…At present, hyperspectral technology is the most widely used technique in the fields of forensic science [9] , such as bloodstain identification [10] and blood age estimation [11] , and biomedicine, such as cell recognition [12] and diagnosis of various diseases [13] . However, the studies on plasma chylous test cases are limited.…”

Section: Prefacementioning

confidence: 99%

Plasma chylous degree detection based on machine learning and hyperspectral techniques

Wei

Liu

Lian

et al. 2022

Preprint

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Blood transfusion is a critical medical treatment, which is performed to save patients’ lives. Chylous blood had high fats. The transfusion of chylous blood into a patient can cause the blockage of micro-vessels. Most blood collection stations are not equipped with the equipment for the detection of chylous blood, and the detection is usually performed with direct observation through the human naked eye, which is prone to certain human errors. Only a few large blood collection stations use the equipment for the detection of chylous blood. In this study, plasma hyperspectral data were collected to detect and identify chylous plasma. The data were preprocessed using the multiple scattering correction (MSC) method and then classified using four classification algorithms, including random forest (RF), K-nearest neighbor KNN), Perceptron, and stochastic gradient descent (SGD) algorithms. First, the healthy and chylous plasma samples were classified into simple dichotomies. The best algorithm was identified by comparing the results of classification algorithms. The results showed that the random forest algorithm-based classification model had the best effect.Then, the chylous plasma was subdivided into different degrees of chylous plasma, which were less separable.A random forest algorithm-based plasma chylous degree detection model was established. Finally, 10 important spectral bands, including 1192.45 nm, 1182.9 nm, 946.98 nm, 1202.01 nm, 1080.93 nm, 1278.41 nm, 1237.03 nm, 991.65 nm, 1020.35 nm, and 1697.8 nm, were selected by band selection. After adjusting the parameters to optimize the model, the prediction accuracy of the whole band was 0.89. This study suggested that hyperspectral technology could identify chylous plasma and could be used to improve its detection efficiency in biomedicine, blood donation centers, human function tests, and other aspects. Filling the gap between machine learning and hyperspectral technology.To provide a new method for the diagnosis of chylous plasma.

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Section: Prefacementioning

confidence: 99%

Plasma chylous degree detection based on machine learning and hyperspectral techniques

Wei

Liu

Lian

et al. 2022

Preprint

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“…The spectral data in the HMI cube reveal the internal microenvironment changes in the samples through parameters such as waveform and intensity, and the microscopic image data can intuitively reflect the sample’s differences in structure with high spatial resolution. At the same time, the combination of HMI and machine learning can assist doctors in diagnosis and greatly improve the efficiency and accuracy of the diagnosis, as well as having a wide field of application in the future [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Liu

et al. 2022

Biosensors

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Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.

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“…Existing monitoring techniques have been reported in the past, but due to several disadvantages, e.g., invasiveness and potential complications, they failed to gain acceptance [12,20,29]. A promising non-invasive, noncontact and non-ionising but cost-intensive method is hyperspectral imaging [21,31,34,39]. Further, telemedicine as a remote service for diagnosis and medical monitoring is a fast-growing public health sector.…”

Section: Introductionmentioning

confidence: 99%

Perfusion assessment via local remote photoplethysmography (rPPG)

Kossack

Wisotzky

Eisert

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signalto-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

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Hyperspectral Imaging for Clinical Applications

Cited by 77 publications

References 98 publications

Plasma chylous degree detection based on machine learning and hyperspectral techniques

Plasma chylous degree detection based on machine learning and hyperspectral techniques

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Perfusion assessment via local remote photoplethysmography (rPPG)

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