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1.
Anal Chim Acta ; 1239: 340710, 2023 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-36628716

RESUMO

The new challenge in the investigation of cultural heritage is the possibility to obtain stratigraphical information about the distribution of the different organic and inorganic components without sampling. In this paper recently commercialized analytical set-up, which is able to co-register VNIR, SWIR, and XRF spectral data simultaneously, is exploited in combination with an innovative multivariate and multiblock high-throughput data processing for the analysis of multilayered paintings. The instrument allows to obtain elemental and molecular information from superficial to subsurface layers across the investigated area. The chemometric strategy proved to be highly efficient in data reduction and for the extraction and integration of the most useful information coming from the three different spectroscopies, also filling the gap between data acquisition and data understanding through the combination of principal component analysis (PCA), brushing, correlation diagrams and maps (within and between spectral blocks) on the low-level fused. In particular, correlation diagrams and maps provide useful information for the reconstruction of a stratigraphic structure without the need to take any sample, thanks to the effective account for inter-correlation among data (variables), which is able to effectively characterize the possible combinations of components located in the same depth level. The highly innovative technology and the data processing strategy are applied for the multi-level characterization of a complex painting reproduction as an illustrative pilot study.


Assuntos
Imageamento Hiperespectral , Pinturas , Projetos Piloto , Análise de Componente Principal , Quimiometria
2.
Arthritis Res Ther ; 25(1): 10, 2023 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-36670487

RESUMO

BACKGROUND/PURPOSE: Lack of robust, feasible, and quantitative outcomes impedes Raynaud phenomenon (RP) clinical trials in systemic sclerosis (SSc) patients. Hyperspectral imaging (HSI) non-invasively measures oxygenated and deoxygenated hemoglobin (oxyHb and deoxyHb) concentrations and oxygen saturation (O2 sat) in the skin and depicts data as oxygenation heatmaps. This study explored the potential role of HSI in quantifying SSc-RP disease severity and activity. METHODS: Patients with SSc-RP (n = 13) and healthy control participants (HC; n = 12) were prospectively recruited in the clinic setting. Using a hand-held camera, bilateral hand HSI (HyperMed™, Waltham, MA) was performed in a temperature-controlled room (22 °C). OxyHb, deoxyHb, and O2 sat values were calculated for 78-mm2 regions of interest for the ventral fingertips and palm (for normalization). Subjects underwent a cold provocation challenge (gloved hand submersion in 15 °C water bath for 1 min), and repeated HSI was performed at 0, 10, and 20 min. Patients completed two patient-reported outcome (PRO) instruments: the Raynaud Condition Score (RCS) and the Cochin Hand Function Scale (CHFS) for symptom burden assessment. Statistical analyses were performed using the Mann-Whitney U test and a mixed effects model (Stata, College Station, TX). RESULTS: Ninety-two percent of participants were women in their 40s. For SSc-RP patients, 69% had limited cutaneous SSc, the mean ± SD SSc duration was 11 ± 5 years, and 38% had prior digital ulcers-none currently. Baseline deoxyHb was higher, and O2 sat was lower, in SSc patients versus HC (p < 0.05). SSc patients had a greater decline in oxyHb and O2 sat from baseline to time 0 (after cold challenge) with distinct rewarming oxyHb, O2 sat, and deoxyHb trajectories versus HCs (p < 0.01). There were no significant correlations between oxyHb, deoxyHb, and O2 sat level changes following cold challenge and RCS or CHFS scores. CONCLUSION: Hyperspectral imaging is a feasible approach for SSc-RP quantification in the clinic setting. The RCS and CHFS values did not correlate with HSI parameters. Our data suggest that HSI technology for the assessment of SSc-RP at baseline and in response to cold provocation is a potential quantitative measure for SSc-RP severity and activity, though longitudinal studies that assess sensitivity to change are needed.


Assuntos
Doença de Raynaud , Esclerodermia Localizada , Escleroderma Sistêmico , Humanos , Feminino , Masculino , Imageamento Hiperespectral , Escleroderma Sistêmico/complicações , Escleroderma Sistêmico/diagnóstico por imagem , Escleroderma Sistêmico/tratamento farmacológico , Doença de Raynaud/diagnóstico por imagem
3.
Spectrochim Acta A Mol Biomol Spectrosc ; 289: 122226, 2023 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-36512964

RESUMO

Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication.


Assuntos
Quimiometria , Cinnamomum zeylanicum , Imageamento Hiperespectral , Análise Discriminante , Análise de Componente Principal , Análise dos Mínimos Quadrados , Máquina de Vetores de Suporte
4.
Food Chem ; 408: 135166, 2023 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-36521293

RESUMO

Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possibility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off-flavor-related compounds. Classification models, including the partial least squares discriminant analysis, least-squares support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were constructed using the full wavelengths and selected spectral features for the identification of off-flavor salmonids. The 1DCNN achieved the highest discrimination accuracy with full and selected wavelengths (i.e., 91.11 and 86.39 %, respectively). Furthermore, the prediction and visualization of off-flavor-related compounds were achieved with acceptable performances (R2 > 0.6) for practical applications. These results indicate the potential of NIR-HSI for the off-flavor profiling of salmonid muscle samples for producers and researchers.


Assuntos
Imageamento Hiperespectral , Espectroscopia de Luz Próxima ao Infravermelho , Espectroscopia de Luz Próxima ao Infravermelho/métodos , Redes Neurais de Computação , Algoritmos , Máquina de Vetores de Suporte , Análise dos Mínimos Quadrados
5.
Food Chem ; 409: 135251, 2023 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-36586261

RESUMO

The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.


Assuntos
Brassica napus , Aprendizado Profundo , Espectroscopia de Luz Próxima ao Infravermelho/métodos , Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Folhas de Planta , Algoritmos
6.
Spectrochim Acta A Mol Biomol Spectrosc ; 289: 122220, 2023 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-36516590

RESUMO

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.


Assuntos
Imageamento Hiperespectral , Espectroscopia de Luz Próxima ao Infravermelho , Animais , Bovinos , Espectroscopia de Luz Próxima ao Infravermelho/métodos , Análise dos Mínimos Quadrados
7.
Sci Rep ; 12(1): 20919, 2022 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-36463337

RESUMO

Tree species' composition of forests is essential in forest management and nature conservation. We aimed to identify the tree species structure of a floodplain forest area using a hyperspectral image. We proposed an efficient novel strategy including the testing of three dimension reduction (DR) methods: Principal Component Analysis, Minimum Noise Fraction (MNF) and Indipendent Component Analysis with five machine learning (ML) algorithms (Maximum Likelihood Classifier, Support Vector Classification, Support Vector Machine, Random Forest and Artificial Neural Network) to find the most accurate outcome; altogether 300 models were calculated. Post-classification was applied by combining the multiresolution segmentation and filtering. MNF was the most efficient DR technique, and at least 7 components were needed to gain an overall accuracy (OA) of > 75%. Forty-five models had > 80% OAs; MNF was 43, and the Maximum Likelihood was 19 times among these models. Best classification belonged to MNF with 10 components and Maximum Likelihood classifier with the OA of 83.3%. Post-classification increased the OA to 86.1%. We quantified the differences among the possible DR and ML methods, and found that even > 10% worse model can be found using popular standard procedures related to the best results. Our workflow calls the attention of careful model selection to gain accurate maps.


Assuntos
Imageamento Hiperespectral , Aprendizado de Máquina , Análise de Componente Principal , Algoritmos
8.
Zhongguo Zhong Yao Za Zhi ; 47(22): 6027-6033, 2022 Nov.
Artigo em Chinês | MEDLINE | ID: mdl-36471926

RESUMO

In order to realize rapid and non-destructive identification of the origin of Gardeniae Fructus, a technical method based on hyperspectral imaging technology was established in this study. Spectral information of Gardeniae Fructus samples from eight production origins was acquired from visible NIR(410-990 nm, VNIR) and short wavelength NIR(950-2 500 nm, SWIR) bands based on hyperspectral imaging techniques. The average spectral reflectance within the region of interest was extracted and calculated using the ENVI 5.3 software, resulting in 1 600 sample data. The visible short wavelength infrared band(fused bands) spectral data covering the range 410-2 500 nm were obtained after combining the spectral data of VNIR and SWIR. Data were de-noised by five common preprocessing methods, including multivariate scatter correction, Savitzky-Golay smoothing, standard normal variate, first derivative(FD), and second derivative from VNIR, SWIR, and fused bands(VNIR+SWIR). Partial least squares discriminant analysis, linear support vector classification(LinearSVC), and random forest were used to establish the model for origin identification of Gardeniae Fructus. The results indicated that the identification model of Gardeniae Fructus origin established after FD pretreatment of the spectral data in the fused bands could yield good results. According to the confusion matrix evaluation results, the model prediction set using LinearSVC reached 100% accuracy, so the optimum identification model of Gardeniae Fructus origin was determined as fusion bands-FD-LinearSVC. Therefore, the hyperspectral imaging technology can achieve rapid, nondestructive, and accurate identification of Gardeniae Fructus samples of different origins, which provides a technical reference for the differential detection of Gardeniae Fructus and other Chinese medicines.


Assuntos
Gardenia , Imageamento Hiperespectral , Frutas , Análise dos Mínimos Quadrados , Tecnologia
9.
Sci Prog ; 105(4): 368504221137461, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36514818

RESUMO

The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the current technologies are difficult to identify colorless transparent film, which becomes an obstacle for the harvest of high-quality cotton. In this paper, an intelligent identification method is proposed to identify the colorless and transparent film on cotton, based on short-wave near-infrared hyperspectral imaging and convolutional neural network (CNN). The algorithm includes black-and-white correction of hyperspectral images, hyperspectral data dimensionality reduction, CNN model training and testing. The key technology is that the features of the hyperspectral image data are degraded by the principal component analysis (PCA) to reduce the amount of computing time. The main innovation is that the colorless and transparent film on cotton can be accurately identified through a CNN with the performance of automatic feature extraction. The experimental results show that the proposed method can greatly improve the identification precision, compared with the traditional methods. After the simulation experiment, the method proposed in this paper has a recognition rate of 98.5% for film. After field testing, the selection rate of film is as high as 96.5%, which meets the actual production needs.


Assuntos
Imageamento Hiperespectral , Redes Neurais de Computação , Análise de Componente Principal , Algoritmos , Filmes Cinematográficos
10.
Molecules ; 27(24)2022 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-36557781

RESUMO

(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R2 = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R2 = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.


Assuntos
Imageamento Hiperespectral , Triticum , Catalase , Máquina de Vetores de Suporte , Redes Neurais de Computação , Algoritmos , Análise dos Mínimos Quadrados
11.
Sensors (Basel) ; 22(24)2022 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-36560046

RESUMO

With the development of deep learning, the use of convolutional neural networks (CNN) to improve the land cover classification accuracy of hyperspectral remote sensing images (HSRSI) has become a research hotspot. In HSRSI semantics segmentation, the traditional dataset partition method may cause information leakage, which poses challenges for a fair comparison between models. The performance of the model based on "convolutional-pooling-fully connected" structure is limited by small sample sizes and high dimensions of HSRSI. Moreover, most current studies did not involve how to choose the number of principal components with the application of the principal component analysis (PCA) to reduce dimensionality. To overcome the above challenges, firstly, the non-overlapping sliding window strategy combined with the judgment mechanism is introduced, used to split the hyperspectral dataset. Then, a PSE-UNet model for HSRSI semantic segmentation is designed by combining PCA, the attention mechanism, and UNet, and the factors affecting the performance of PSE-UNet are analyzed. Finally, the cumulative variance contribution rate (CVCR) is introduced as a dimensionality reduction metric of PCA to study the Hughes phenomenon. The experimental results with the Salinas dataset show that the PSE-UNet is superior to other semantic segmentation algorithms and the results can provide a reference for HSRSI semantic segmentation.


Assuntos
Artrópodes , Imageamento Hiperespectral , Animais , Semântica , Algoritmos , Julgamento , Processamento de Imagem Assistida por Computador
12.
Bioinspir Biomim ; 18(1)2022 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-36541456

RESUMO

In the underwater environment, conventional hyperspectral imagers for imaging target scenes usually require stable carrying platforms for completing push sweep or complex optical components for beam splitting in long gaze imaging, which limits the system's efficiency. In this paper, we put forward a novel underwater hyperspectral imaging (UHI) system inspired by the visual features of typical cephalopods. We designed a visual bionic lens which enlarged the chromatic blur effect to further ensure that the system obtained blur images with high discrimination of different bands. Then, chromatic blur datasets were collected underwater to complete network training for hyperspectral image reconstruction. Based on the trained model, our system only required three frames of chromatic blur images as input to effectively reconstruct spectral images of 30 bands in the working light range from 430 nm to 720 nm. The results showed that the proposed hyperspectral imaging system exhibited good spectral imaging potential. Moreover, compared with the traditional gaze imaging, when obtaining similar hyperspectral images, the data sampling rate in the proposed system was reduced by 90%, and the exposure time of required images was only about 2.1 ms, reduced by 99.98%, which can greatly expand its practical application range. This experimental study illustrates the potential of chromatic blur vision for UHI, which can provide rapid response in the recognition task of some underwater dynamic scenarios.


Assuntos
Diagnóstico por Imagem , Imageamento Hiperespectral , Processamento de Imagem Assistida por Computador
13.
Sensors (Basel) ; 22(24)2022 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-36560152

RESUMO

Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network.


Assuntos
Algoritmos , Imageamento Hiperespectral , Humanos , Análise de Componente Principal , China , Distribuição Normal
14.
Sci Rep ; 12(1): 21141, 2022 12 07.
Artigo em Inglês | MEDLINE | ID: mdl-36476976

RESUMO

Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging.


Assuntos
Imageamento Hiperespectral , Viagem
15.
Sci Rep ; 12(1): 21140, 2022 12 07.
Artigo em Inglês | MEDLINE | ID: mdl-36477460

RESUMO

This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400-1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model's prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.


Assuntos
Imageamento Hiperespectral , Nível de Saúde
16.
Scand J Trauma Resusc Emerg Med ; 30(1): 66, 2022 Dec 09.
Artigo em Inglês | MEDLINE | ID: mdl-36494726

RESUMO

BACKGROUND: Hyperspectral imaging (HSI) is a novel imaging technology with the ability to assess microcirculatory impairment. We aimed to assess feasibility of performing HSI, a noninvasive, contactless method to assess microcirculatory alterations, during trauma resuscitation care. METHODS: This randomized controlled clinical trial was conducted in a dedicated trauma resuscitation room of a level one trauma center. We included adult patients who were admitted to the trauma resuscitation room. Patients were allocated in a 1:1 ratio to the HSI group (intervention) or control group. In addition to the standard of care, patients in the intervention group had two hyperspectral recordings (HSR) of their hand palm taken. Primary outcomes were the treatment duration of the primary survey (until end of ABCDE-evaluation, ultrasound and evaluation by the trauma team) and the total resuscitation room care (until transport to definitive care) as well as the ability to perform measurements from all HSR. Secondary outcomes were analyses from the intervention group compared to HSI measurements of 26 healthy volunteers including an analysis based on the ISS (Injury severity score) (< 16 vs. ≥ 16). Care givers, and those assessing the outcomes were blinded to group assignment. RESULTS: Our final analysis included 51 patients, with 25 and 26 allocated to the control and intervention group, respectively. There was a statistically significant shorter median duration of the primary survey in the control group (03:22 min [Q1-Q3 03:00-03:51]) compared to the intervention group (03:59 min [Q1-Q3 03:29-04:35]) with a difference of -37 s (95% CI -66 to -12). Total resuscitation room care was longer in the control group, but without significance: 60 s (95% CI -60 to 180). From 52 HSI, we were able to perform hyperspectral measurements on all images, with significant differences between injured patients and healthy volunteers. CONCLUSION: HSI proved to be feasible during resuscitation room care and can provide valuable information on the microcirculatory state. Trial registration DRKS DRKS00024047- www.drks.de . Registered on 13th April 2021.


Assuntos
Imageamento Hiperespectral , Ressuscitação , Adulto , Humanos , Microcirculação , Ressuscitação/métodos , Escala de Gravidade do Ferimento , Centros de Traumatologia
17.
Sci Rep ; 12(1): 19757, 2022 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-36396749

RESUMO

Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.


Assuntos
Oryza , Doenças das Plantas , Imageamento Hiperespectral , Redes Neurais de Computação , Oryza/microbiologia , Doenças das Plantas/microbiologia , Folhas de Planta
18.
Opt Express ; 30(23): 41590-41612, 2022 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-36366633

RESUMO

Optical water classification based on remote sensing reflectance (Rrs(λ)) data can provide insight into water components driving optical variability and inform the development and application of bio-optical algorithms in complex aquatic systems. In this study, we use an in situ dataset consisting of hyperspectral Rrs(λ) and other biogeochemical and optical parameters collected over nearly five years across a heavily urbanized estuary, the Long Island Sound (LIS), east of New York City, USA, to optically classify LIS waters based on Rrs(λ) spectral shape. We investigate the similarities and differences of discrete groupings (k-means clustering) and continuous spectral indexing using the Apparent Visible Wavelength (AVW) in relation to system biogeochemistry and water properties. Our Rrs(λ) dataset in LIS was best described by three spectral clusters, the first two accounting for the majority (89%) of Rrs(λ) observations and primarily driven by phytoplankton dynamics, with the third confined to measurements in river and river plume waters. We found AVW effective at tracking subtle changes in Rrs(λ) spectral shape and fine-scale water quality features along river-to-ocean gradients. The recently developed Quality Water Index Polynomial (QWIP) was applied to evaluate three different atmospheric correction approaches for satellite-derived Rrs(λ) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor in LIS, finding Polymer to be the preferred approach. Our results suggest that integrative, continuous indices such as AVW can be effective indicators to assess nearshore biogeochemical variability and evaluate the quality of both in situ and satellite bio-optical datasets, as needed for improved ecosystem and water resource management in LIS and similar regions.


Assuntos
Ecossistema , Estuários , Monitoramento Ambiental/métodos , Imageamento Hiperespectral , Rios/química
19.
Sci Rep ; 12(1): 18475, 2022 Nov 02.
Artigo em Inglês | MEDLINE | ID: mdl-36323727

RESUMO

One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.


Assuntos
Algoritmos , Imageamento Hiperespectral , Iluminação
20.
J Biomed Opt ; 27(10)2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-36316301

RESUMO

SignificanceMalignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures.AimTumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL.ApproachAn HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework.ResultsCross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border.ConclusionsGood performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.


Assuntos
Melanoma , Neoplasias Cutâneas , Humanos , Redes Neurais de Computação , Imageamento Hiperespectral , Melanoma/diagnóstico por imagem , Melanoma/patologia , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/patologia , Diagnóstico por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos
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