Relevant bibliographies by topics / Fluorescence hyperspectral imaging (fHSI)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Fluorescence hyperspectral imaging (fHSI)'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Fluorescence hyperspectral imaging (fHSI)"

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Luthman, Anna Siri, Sebastian Dumitru, Isabel Quiros-Gonzalez, James Joseph, and Sarah E. Bohndiek. "Fluorescence hyperspectral imaging (fHSI) using a spectrally resolved detector array." Journal of Biophotonics 10, no. 6-7 (2017): 840–53. http://dx.doi.org/10.1002/jbio.201600304.

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Wang, Chengzhi, Xiaping Fu, Ying Zhou, and Feng Fu. "Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique." Foods 13, no. 6 (2024): 897. http://dx.doi.org/10.3390/foods13060897.

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Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.
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Zhan, Chunyi, Hongyi Mao, Rongsheng Fan, et al. "Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning." Foods 13, no. 22 (2024): 3547. http://dx.doi.org/10.3390/foods13223547.

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China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440–530 nm and 680–780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple’s equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.
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Hu, Yan, Youli Wu, Jie Sun, Jinping Geng, Rongsheng Fan, and Zhiliang Kang. "Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics." Foods 11, no. 15 (2022): 2344. http://dx.doi.org/10.3390/foods11152344.

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Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS–DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS–DA. MSC_VIP_PLS–DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.
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Beule, Pieter De, Dylan M. Owen, Hugh B. Manning, et al. "Rapid hyperspectral fluorescence lifetime imaging." Microscopy Research and Technique 70, no. 5 (2007): 481–84. http://dx.doi.org/10.1002/jemt.20434.

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Nie, Zhaojun, Ran An, Joseph E. Hayward, Thomas J. Farrell, and Qiyin Fang. "Hyperspectral fluorescence lifetime imaging for optical biopsy." Journal of Biomedical Optics 18, no. 9 (2013): 096001. http://dx.doi.org/10.1117/1.jbo.18.9.096001.

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Juntunen, Cory, Isabel M. Woller, and Yongjin Sung. "Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography." Sensors 21, no. 11 (2021): 3652. http://dx.doi.org/10.3390/s21113652.

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Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.
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Safonov, А. I., K. V. Nikolaev, and S. N. Yakunin. "Hyperspectral X-ray imaging for nanometrology." Kristallografiâ 69, no. 4 (2024): 730–42. http://dx.doi.org/10.31857/s0023476124040207.

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A tool for X-ray hyperspectral imaging has been developed. It is based on a conventional CCD driven by an algorithm that allows resolution in both energy and position. A new algorithm has been developed that allows the real-time analysis of single photon events. The factors influencing the energy resolution, the formation of artifacts in the energy spectra, and the counting efficiency are analyzed. Furthermore, a method for achieving sub-pixel precision using the singular value decomposition is suggested. The algorithm has been tested on synthetic data and in a live experiment with the registration of X-ray fluorescence emission from a thin film structure. Applying hyperspectral imaging to grazing emission X-ray fluorescence opens up new possibilities in nanometrology.
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Di Benedetto, Alessia, Luìs Manuel de Almieda Nieto, Alessia Candeo, Gianluca Valentini, Daniela Comelli, and Matthias Alfeld. "Multivariate analysis on fused hyperspectral datasets within Cultural Heritage field." EPJ Web of Conferences 309 (2024): 14007. http://dx.doi.org/10.1051/epjconf/202430914007.

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This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix to highlight components and reduce dimensionality, with techniques such as Non-negative Matrix Factorization (NMF) gaining prominence. To maximize the synergies between multimodal datasets, the fusion of hyperspectral datasets can be coupled with multivariate analysis, with potential applications in CH. In this work, I will show examples of this approach with different combinations of datasets, including reflectance and transmittance spectral imaging, Fluorescence Lifetime Imaging and Time-Gated Hyperspectral Imaging, and Raman and fluorescence spectroscopy micro-mapping.
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Kong, Seong G., Matthew E. Martin, and Tuan Vo-Dinh. "Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection." ETRI Journal 28, no. 6 (2006): 770–76. http://dx.doi.org/10.4218/etrij.06.0106.0061.

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Dissertations / Theses on the topic "Fluorescence hyperspectral imaging (fHSI)"

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Tendenes, Nils Ove. "LED light source for hyperspectral fluorescence imaging." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19193.

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This report deals with the possibility of creating a LED light source, to be used withhyperspectral fluorescence imaging. There are commercially available light sources thatcould be used, but they are expensive, they do not necessarily emit the right wavelength, the uniformity of the field is questionable and they are difficult to modify.First a batch of Light emitting diodes were acquired, these were subjected to a seriesof tests to classify their limitations and determine which diodes were to be included in the final light source. A spectrometer was used to determine the emitted wavelength of each diode and which scenarios could change the wavelength of the emitted light. Aphotodiode was used to acquire the viewing angle of the LEDs and their relative radiantpower. Images gathered by a hyperspectral camera were used to determine the relevancyof noise produced by the current source. When the light emitting diodes were chosen,the photodiode was used to make an image of the light field. The final light source wasmounted on the hyperspectral camera to gather fluorescent images.The final tests revealed a fully functional light source with potential to be used on aregular basis, but the current source was too cumbersome and the field was not optimal.These are issues that can be dealt with and this light source can in the future provide a cheap and easily modifiable light source alternative.
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Luthman, Anna Siri Naemi. "Spectrally resolved detector arrays for multiplexed biomedical fluorescence imaging." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274904.

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The ability to resolve multiple fluorescent emissions from different biological targets in video rate applications, such as endoscopy and intraoperative imaging, has traditionally been limited by the use of filter-based imaging systems. Hyper and multispectral imaging facilitate the detection of both spatial and spectral information in a single data acquisition, however, instrumentation for spatiospectral data acquisition is typically complex, bulky and expensive. This thesis seeks to overcome these limitations by using recently commercialised compact and robust hyper/multispectral cameras based on spectrally resolved detector arrays. Following sensor calibrations, which devoted particular attention to the angular sensitivity of the sensors, we integrated spectrally resolved detector arrays into a wide-field and an endoscopic imaging platform. This allowed multiplexed reflectance and fluorescence imaging with spectrally resolved detector array technology in vitro, in tissue mimicking phantoms, in an ex vivo oesophageal model and in vivo in a mouse model. A hyperspectral linescan sensor was first integrated in a wide-field near-infrared reflectance based imaging set-up to assess the suitability of spectrally resolved detector arrays for in vivo imaging of exogenous fluorescent contrast agents. Using this fluorescence hyperspectral imaging system, we could accurately resolve the presence and concentration of seven fluorescent dyes in solution. We also demonstrated high spectral unmixing precision, signal linearity with dye concentration, at depth in tissue mimicking phantoms, and delineation of four fluorescent dyes in vivo. After the successful demonstration of multiplexed fluorescence imaging in a wide-field set-up, we proceeded to combine near-infrared multiplexed fluorescence imaging with visible light spectral reflectance imaging in an endoscopic set-up. A multispectral endoscopic imaging system, capable of simultaneous reflectance and fluorescence imaging, was developed around two snapshot spectrally resolved detector arrays. In the process of system integration and characterisation, methods to characterise and predict the imaging performance of spectral endoscopes were developed. With the endoscope we demonstrated simultaneous imaging and spectral unmixing of chemically oxy/deoxygenated blood and three fluorescent dyes in a tissue mimicking phantom, and of two fluorescent dyes in an ex vivo oesophageal porcine model. With further developments, this technology has the potential to become applicable in medical imaging for detection of diseases such as gastrointestinal cancers.
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Barberio, Manuel. "Real-time intraoperative quantitative assessment of gastrointestinal tract perfusion using hyperspectral imaging (HSI)." Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAJ120.

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La fistule anastomotique (FA) est une complication grave de la chirurgie. Une perfusion locale adéquate est fondamentale pour réduire le risque de FA. Cependant, les critères cliniques ne sont pas fiables pour évaluer la perfusion intestinale. À cet égard, l'angiographie par fluorescence (AF) a été explorée. Malgré des résultats prometteurs dans les essais cliniques, l'évaluation de l'AF est subjective, d'où l'incertitude quant à son efficacité. L'AF quantitative a déjà été introduite. Cependant, elle est limitée par la nécessité d'injecter un fluorophore. L'imagerie hyperspectrale (HSI) est une technique d'imagerie optique prometteuse couplant un spectroscope à une caméra photo, permettant une analyse quantitative des tissus en temps réel et sans contraste. L'utilisation intraopératoire de l'HSI est limitée par la présence d'images statiques. Nous avons développé la hyperspectral-based enhanced reality (HYPER), pour permettre une évaluation précise de la perfusion intraopératoire. Cette thèse décrit les étapes du développement et de la validation d'HYPER<br>Anastomotic leak (AL) is a severe complication in surgery. Adequate local perfusion is fundamental to promote anastomotic healing, reducing the risk of AL. However, clinical criteria are unreliable to evaluate bowel perfusion. Consequently, a tool allowing to objectively detect intestinal viability intraoperatively is desirable. In this regard, fluorescence angiography (FA) has been explored. In spite of promising results in clinical trials, FA assessment is subjective, hence the efficacy of FA is unclear. Quantitative FA has been previously introduced. However, it is limited by the need of injecting a fluorophore. Hyperspectral imaging (HSI) is a promising optical imaging technique coupling a spectroscope with a photo camera, allowing for a contrast-free, real-time, and quantitative tissue analysis. The intraoperative usability of HSI is limited by the presence of static images. We developed hyperspectral-based enhanced reality (HYPER), to allow for precise intraoperative perfusion assessment. This thesis describes the steps of the development and validation of HYPER
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Gouisset, Emmanuel. "Développement et étude de la réponse instrumentale d'un imageur hyperspectral large bande (UV-Visible-NIR) permettant la caractérisation physico-chimique de contaminants sur surfaces sensibles d’engins orbitaux." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS139.

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Dans le domaine de l’analyse de défaillance et plus particulièrement la contamination moléculaire et particulaire, il est crucial de pouvoir détecter toute trace de contaminants durant l’intégration d’un engin orbital. Dans ce contexte, la fluorescence permet non seulement de détecter mais aussi de discriminer les contaminants. Pour ce projet, nous avons donc développé un instrument hyperspectral large-bande (UV-Vis-NIR) de 330 à 1000 nm pour pouvoir détecter un large panel de contaminants. Il s’agit d’un montage catoptrique permettant de s’affranchir des aberrations chromatiques. Il présente un champ de vue de 3,5° pour une résolution angulaire de 25 secondes d’arc. Il a été conçu pour être portable et son ensemble mécanique figé permet un alignement optique simple à mettre en œuvre et une réalisation rapide des fichiers de calibration entre deux scènes. Nous avons mesuré une résolution spectrale de 1 nm dans l’UV, 2 à 3 nm dans le visible et 5 nm dans le NIR. Cela nous a permis d’étudier la réponse en fluorescence de deux colles époxy, sources typiques de la contamination d’engin orbital et de la comparer avec une mesure obtenue avec un instrument commercial. Ces mesures nous ont permis d’évaluer les performances de notre instrument et d’identifier des perspectives d’amélioration, notamment en termes de sensibilité dans les UV<br>In the field of failure analysis and in particular molecular and particulate contamination, being able to detect any trace of contaminants during the integration of an orbital spacecraft is crucial. In this context, fluorescence allows not only to detect but also to discriminate contaminants. For this project, we have therefore developed a broadband hyperspectral instrument (UV-Vis-NIR) from 330 to 1000 nm to be able to detect a wide range of contaminants. It is a catoptric assembly that eliminates chromatic aberrations. The field of view is 3.5° for an angular resolution of 25 arc seconds. It was designed to be portable and its fixed mechanical assembly allows easy optical alignment and rapid creation of calibration files between two scenes. We measured a spectral resolution of 1 nm in the UV range, 2 to 3 nm in the visible range and 5 nm in the NIR range. This allowed us to study the fluorescence response of two epoxy glues, typical sources of orbital spacecraft contamination, and to compare it with a measurement obtained with a commercial instrument. These measurements allowed us to evaluate the performance of our instrument and identify prospects of improvement, especially in terms of sensitivity in UV range
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Jahr, Wiebke. "Spectrally resolved, three-dimensional widefield microscopy." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-225963.

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A major goal in biological imaging is to visualize interactions of different tissues, often fluorescently labeled, during dynamic processes. Only a few of these labels fit into the available spectral range without overlap, but can be separated computationally if the full spectrum of every single pixel is known. In medical imaging, hyperspectral techniques show promise to identify different tissue types without any staining. Yet, microscopists still commonly acquire spectral information either with filters, thus integrating over a few broad bands only, or point-wise, dispersing the spectra onto a multichannel detector, which is inherently slow. Light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) are two techniques to acquire 3D microscopic data fast, photon-efficiently and gently on the specimen. LSFM works in fluorescence mode and OPT in transmission. Both are based on a fast widefield detection scheme where a 2D detector records the spatial information but leaves no room to acquire dispersed spectra. Hyperspectral imaging had not yet been demonstrated for either technique. In this work, I developed a line-scanning hyperspectral LSFM and an excitation scanning OPT to acquire 5D data (3D spatial, 1D temporal, 1D spectral) and optimized the performance of both setups to minimize acquisition times without sacrificing image contrast, spatial or spectral information. I implemented and assessed different evaluation pipelines to classify and unmix relevant features. I demonstrate the efficiency of my workflow by acquiring up to five fluorescent markers and the autofluorescence in \\zf and fruit fly embryos on my hyperspectral LSFM. I extracted both concentration maps and spectra for each of these fluorophores from the multidimensional data. The same methods were applied to investigate the transmission data from my spectral OPT, where I found evidence that OPT image formation is governed by refraction, whereas scattering and absorption only play a minor role. Furthermore, I have implemented a robust, educational LSFM on which laymen have explored the working principles of modern microscopies. This eduSPIM has been on display in the Technische Sammlungen Dresden for one year during the UNESCO international year of light<br>Ein wichtiges Ziel biologischer Bildgebung ist die Visualisierung des Zusammenspiels von verschiedenen, meist fluoreszent markierten, Geweben bei dynamischen Prozessen. Nur wenige dieser Farbstoffe passen ohne Überlapp in das zur Verfügung stehende Spektrum. Sie können jedoch rechnerisch getrennt werden, wenn das gesamte Spektrum jedes Pixels bekannt ist. In medizinischen Anwendungen versprechen hyperspektrale Techniken, verschiedene Gewebetypen markierungsfrei zu identifizieren. Dennoch ist es in der Mikroskopie noch immer üblich, spektrale Information entweder mit Filtern über breiten Bändern zu integrieren, oder Punktspektren mithilfe von Dispersion zu trennen und auf einem Multikanaldetektor aufzunehmen, was inhärent langsam ist. Light Sheet Fluorescence Microscopy (LSFM) und Optical Projection Tomography (OPT) nehmen 3D Mikroskopiedaten schnell, photoneneffizient und sanft für die Probe auf. LSFM arbeitet mit Fluoreszenz, OPT in Transmission. Beide basieren auf schneller Weitfelddetektion, wobei die räumliche Information mit einem 2D Detektor aufgenommen wird, der keinen Raum lässt, um die getrennten Spektren zu messen. Hyperspektrale Bildgebung wurde bis jetzt für keine der zwei Techniken gezeigt. Ich habe ein hyperspektrales LSFM mit Linienabtastung und ein OPT mit Wellenlängenabtastung entwickelt, um 5D Daten (3D räumlich, 1D zeitlich, 1D spektral) aufzunehmen. Beide Aufbauten wurden hinsichtlich minimaler Aufnahmezeit optimiert, ohne dabei Kontrast, räumliche oder spektrale Auflösung zu opfern. Ich habe verschiedene Abläufe zum Klassifizieren und Trennen der Hauptkomponenten implementiert. Ich nehme bis zu fünf Fluorophore und Autofluoreszenz in Zebrafisch- und Fruchtfliegenembryos mit dem hyperspektralen LSFM auf und zeige die Effizienz des gesamten Ablaufes, indem ich Spektren und räumliche Verteilung aller Marker extrahiere. Die Transmissionsdaten des spektralen OPT werden mit denselben Methoden untersucht. Ich konnte belegen, dass die Bildformation im OPT massgeblich von Brechung bestimmt ist, und Streuung und Absorption nur einen geringen Beitrag leisten. Außerdem habe ich ein robustes, didaktisches LSFM gebaut, damit Laien die Funktionsweise moderner Mikroskopie erkunden können. Dieses eduSPIM war ein Jahr lang in den Technischen Sammlungen Dresden ausgestellt
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Henrot, Simon. "Déconvolution et séparation d'images hyperspectrales en microscopie." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00931579.

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L'imagerie hyperspectrale consiste à acquérir une scène spatiale à plusieurs longueurs d'onde, de manière à reconstituer le spectre des pixels. Cette technique est utilisée en microscopie pour extraire des informations spectrales sur les spécimens observés. L'analyse de telles données est bien souvent difficile : lorsque l'image est observée à une résolution suffisamment fine, elle est dégradée par l'instrument (flou ou convolution) et une procédure de déconvolution doit être utilisée pour restaurer l'image originale. On parle ainsi de problème inverse, par opposition au problème direct consistant à modéliser la dégradation de l'image observée, étudié dans la première partie de la thèse. Un autre problème inverse important en imagerie consiste à extraire les signatures spectrales des composants purs de l'image ou sources et à estimer les contributions fractionnaires de chaque source à l'image. Cette procédure est qualifiée de séparation de sources, accomplie sous contrainte de positivité des sources et des mélanges. La deuxième partie propose des contributions algorithmiques en restauration d'images hyperspectrales. Le problème de déconvolution est formulé comme la minimisation d'un critère pénalisé et résolu à l'aide d'une structure de calcul rapide. Cette méthode générique est adaptée à la prise en compte de différents a priori sur la solution, tels que la positivité de l'image ou la préservation des contours. Les performances des techniques proposées sont évaluées sur des images de biocapteurs bactériens en microscopie confocale de fluorescence. La troisième partie de la thèse est axée sur la problématique de séparation de sources, abordé dans un cadre géométrique. Nous proposons une nouvelle condition suffisante d'identifiabilité des sources à partir des coefficients de mélange. Une étude innovante couplant le modèle d'observation instrumental avec le modèle de mélange de sources permet de montrer l'intérêt de la déconvolution comme étape préliminaire de la séparation. Ce couplage est validé sur des données acquises en microscopie confocale Raman.
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Henrot, Simon. "Déconvolution et séparation d'images hyperspectrales en microscopie." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0187.

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L'imagerie hyperspectrale consiste à acquérir une scène spatiale à plusieurs longueurs d'onde, e.g. en microscopie. Cependant, lorsque l'image est observée à une résolution suffisamment fine, elle est dégradée par un flou (convolution) et une procédure de déconvolution doit être utilisée pour restaurer l'image originale. Ce problème inverse, par opposition au problème direct modélisant la dégradation de l'image observée, est étudié dans la première partie . Un autre problème inverse important en imagerie, la séparation de sources, consiste à extraire les spectres des composants purs de l'image (sources) et à estimer les contributions de chaque source à l'image. La deuxième partie propose des contributions algorithmiques en restauration d'images hyperspectrales. Le problème est formulé comme la minimisation d'un critère pénalisé et résolu à l'aide d'une structure de calcul rapide. La méthode est adaptée à la prise en compte de différents a priori sur l'image, tels que sa positivité ou la préservation des contours. Les performances des techniques proposées sont évaluées sur des images de biocapteurs bactériens en microscopie confocale de fluorescence. La troisième partie est axée sur le problème de séparation de sources, abordé dans un cadre géométrique. Nous proposons une nouvelle condition suffisante d'identifiabilité des sources à partir des coefficients de mélange. Une étude innovante couplant le modèle d'observation avec le mélange de sources permet de montrer l'intérêt de la déconvolution comme étape préliminaire de la séparation. Ce couplage est validé sur des données acquises en spectroscopie Raman<br>Hyperspectral imaging refers to the acquisition of spatial images at many spectral bands, e.g. in microscopy. Processing such data is often challenging due to the blur caused by the observation system, mathematically expressed as a convolution. The operation of deconvolution is thus necessary to restore the original image. Image restoration falls into the class of inverse problems, as opposed to the direct problem which consists in modeling the image degradation process, treated in part 1 of the thesis. Another inverse problem with many applications in hyperspectral imaging consists in extracting the pure materials making up the image, called endmembers, and their fractional contribution to the data or abundances. This problem is termed spectral unmixing and its resolution accounts for the nonnegativity of the endmembers and abundances. Part 2 presents algorithms designed to efficiently solve the hyperspectral image restoration problem, formulated as the minimization of a composite criterion. The methods are based on a common framework allowing to account for several a priori assumptions on the solution, including a nonnegativity constraint and the preservation of edges in the image. The performance of the proposed algorithms are demonstrated on fluorescence confocal images of bacterial biosensors. Part 3 deals with the spectral unmixing problem from a geometrical viewpoint. A sufficient condition on abundance coefficients for the identifiability of endmembers is proposed. We derive and study a joint observation model and mixing model and demonstrate the interest of performing deconvolution as a prior step to spectral unmixing on confocal Raman microscopy data
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Su, Poyu, and 蘇柏宇. "Chlorophyll Two-photon Fluorescence Hyperspectral Imaging Applied to Photosynthesis Research." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/8er84x.

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碩士<br>國立中央大學<br>光電科學與工程學系<br>104<br>Chlorophyll fluorescence is considered as a non-invasive method to monitor the physiological state of plants and is capable to apply for higher plants researches. A home-built line scanning two-photon fluorescence hyperspectral microscopy is used to record the chlorophyll fluorescence hyperspectral images containing 3D spatial and 1D spectral information in this research. The hyperspectral images within 120 μm beneath the leaf surface can be recorded. In this research, the principle component analysis method and cryogenic fluorescence spectrum measurement are applied to analyze the spectrum bases of Photosystem I and Photosystem II. To correct the re-absorption effect in spectrum analysis, absorption spectrum of intact leaf is measured and used to build the fitting function. Using least squares method, the fluorescence spectra of mesophyll cells at various locations in three-dimensional are fitted and analyzed. Through the errors between the measured and fitted fluorescence spectra, the fitting method used in this research is verified to be applicable for analysis of chlorophyll fluorescence.
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Chen, Wei-Tung, and 陳威同. "Observation and Measurement of Residual Bran on Milled Rice Surface Using Hyperspectral Imaging and Fluorescence Imaging." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/h4me5t.

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碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>102<br>Rice is typically consumed after milling, a process of removing the husk and bran layers on rice surface. The degree of bran residue remaining on rice surface after milling directly affects the rice quality. And the bran layer of rice mainly composed of lipids. This work proposed to nondestructively detect bran residue on single rice grain using hyperspectral imaging (HSI) and fluorescence imaging (FRI). HSI and FRI are sensing techniques that combines both spatial and spectral information and may be used for chemical compound identification and quantification. In the HSI experiment, rice samples were milled and scanned using an HSI system. Afterward, the rice samples were dyed to enable the residual bran to be identified using optical microscopy and image processing algorithms. Classifiers were then developed to predict the rice bran residue by using the HSI measurements as inputs. In the FRI experiment, appropriate combinations of fluorescence excitation and emission wavelengths were identified. Fluorescence images of rice samples at these excitation and emission wavelength combinations were then acquired and were used as the inputs to the machine learning classifier. After that, the same staining procedure and model development performed in the HSI work were applied. Bran image were predicted by using the fluorescence images. The predicted images were compared with the micrograph images for classifier performance evaluation. The proposed HSI and FRI approaches could reasonably estimate the residual bran distribution on milled rice surface.
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Chen, Li-Ta, and 陳立達. "Application of Hyperspectral Fluorescence Imaging toAssess Characteristics of Animal and Plant Oil Mixture." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/487yvx.

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碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>106<br>In recent years there’s lots of food safety issue around the world, according to the paper review and market survey we conclude that oil adulteration is one of the most commonly event in actual problems, whereas traditional inspection process takes time and laborious so as a respond this research aims to discover the potential of using Hyperspectral fluorescence imaging to assess the oil concentration. In this research, Ghee is the adulteration target, which will be adulterated with Rice bran, Canola, Olive oil in five concentration level (100%, 75%, 50%, 25%, 0%) respectively, first the use of Excitation and emission matrix gives the information about what exact excitation light should be used to induce fluorescence, use this excitation as a light source in Hyperspectral imaging system to get the fluorescence spectrum for each oil mixture, and build the ANN model to classify the adulterated type, for pure Ghee, adulterated Rice bran, Canola, Olive oil the accuracy approaching 7.33%, 99.50%, 100.00%, 68.25% respectively, SVR model for predicting the Ghee concentration in oil mixture reaching averaging error 88.53%, 97.4%, 82.13%, 59.5% respectively, furthermore, if also takes the different brand factor for each plant oil into account, the accuracy for ANN still retain 100%, 96%, 93.5% respectively, for SVR model we have averaging error 15.00%, 7.61%, 11.31% respectively. For ANN classification accuracy of portable detection device also reaching 97.00%, 100.00%, 100.00%, 100.00%, for SVR model also reaching average error 7.18%, 10.16%, 11.92%, respectively.
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Book chapters on the topic "Fluorescence hyperspectral imaging (fHSI)"

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Zhongzhi, Han. "Aflatoxin Detection by Fluorescence Index and Narrowband Spectra Based on Hyperspectral Imaging." In Computer Vision-Based Agriculture Engineering. CRC Press, 2019. http://dx.doi.org/10.1201/9780429289460-2.

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Gupta, Neelam. "Biosensors Technologies: Acousto-Optic Tunable Filter-Based Hyperspectral and Polarization Imagers for Fluorescence and Spectroscopic Imaging." In Biosensors and Biodetection. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-567-5_16.

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Tummers, Anna, Arie Wallert, Katja Kleinert, et al. "Case Study 1: Portrait of a Young Woman: Assessing New Technologies." In Cultural Heritage Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59489-2_3.

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AbstractThis case study evaluates the respective use of five non-destructive techniques that are at our disposal to research the characteristic style and painting technique of Frans Hals. For this purpose, a typical Frans Hals portrait of around 1635, Portrait of a Woman in the Gemäldegalerie in Berlin, was researched using not only observations with the naked eye, x-rays (XR) and infrared reflectography (IRR), but also macro-x-ray fluorescence scans (MA-XRF), hyperspectral imaging also known as reflectance imaging spectroscopy (HI/RIS), and even neutron activated radiographs (NAR). As the portrait is the only painting to date to have been researched with all these techniques, it provided a unique opportunity to compare their respective merits and assess their potential.
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Tummers, Anna, Arie Wallert, Robert G. Erdmann, et al. "Case Study 2: The New York Malle Babbe (‘Mad Barbara’): Original, Studio Work or Forgery?" In Cultural Heritage Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59489-2_4.

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AbstractQuite a few of the most innovative and best-known paintings by Frans Hals exist in several variants. Attributing some of these versions or imitations is a notoriously difficult challenge. A case in point is the Malle Babbe painting at the Metropolitan Museum in New York, which has a rich attribution history: it has been called an original Frans Hals, a work by one of his sons, an early imitation with a forged signature and even a modern forgery (Fig. 4.1). This case study sheds new light on its attribution by comparing its style, technique and materials in depth to the well-known original by Frans Hals in Berlin, to the Malle Babbe forgery created by Han van Meegeren at the Rijksmuseum in Amsterdam, and by relating it to relevant primary sources and seventeenth-century art theory. New technical research was done on all three paintings specifically for this study, including infrared reflectography (IRR), macro X-ray fluorescence scanning (MA-XRF), hyperspectral imaging or reflectance imaging spectroscopy (HI/RIS) and lead isotope analysis. Advanced digital tools were developed to aid the comparison (sn.pub/3xs1ac; https://images.erdmann.io/Draper/?manifest=/NICAS/Frans_Hals/image_manifest_MB.json).
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Pane, Catello. "Advances in proximal sensors to detect crop health status in horticultural crops." In Improving integrated pest management in horticulture. Burleigh Dodds Science Publishing, 2022. http://dx.doi.org/10.19103/as.2021.0095.06.

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Advances in proximal sensing for targeted application of bioprotectants and biopesticides are crucial for more sustainable horticultural crop disease management. These technologies can help to identify spatial/temporal patterns of disease and provide data for forecasting models. They rely on optoelectronic devices which capture reflected and/or emitted energy from plants, using imaging and/or non-imaging technologies for rapid, non-destructive and contactless diagnosis of disease symptoms. This chapter discusses hyperspectral, multispectral and RGB sensors, thermographic and chlorophyll fluorescence probes. The chapter also discusses about the application of sensors detecting micro-environmental parameters linked to the pathogen lifecycles, which can then be used to predict disease risk.
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Kumar, Ritesh, Shikha Pathak, Nishant Prakash, Upasna Priya, and Abhijeet Ghatak. "Application of Spectroscopic Techniques in Early Detection of Fungal Plant Pathogens." In Diagnostics of Plant Diseases. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97535.

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Among the plant pathogens, around 85% of diseases in plants are caused by fungi. Rapid and accurate detection of fungal phytopathogens up to the species level is crucial for the implementation of proper disease control strategies, which were previously relied on conventional approaches. The conventional identification methods have been replaced by many rapid and accurate methods like high throughput sequencing, real-time polymerase chain reaction (PCR), serological and spectroscopic technique. Among these rapid pathogen detection techniques, spectroscopy is a rapid, cost-effective, non-destructive method and does not require sample preparation. Nowadays, visible, infrared and near-infrared rays are commonly employed for pathogen detection. Fluorescence Spectroscopy, Nuclear Magnetic Resonance (NMR) spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Attenuated Total Reflection (ATR)-FTIR spectroscopy, Raman Spectroscopy, Matrix-assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS). Biocontrol fungus-like Trichoderma spp. can be detected with the help of MALDI-TOF MS. Fluorescence spectroscopy used fluorescence emanating from the sample and successfully used in the detection of powdery mildew (Blumeria graminis). Hyperspectral imaging is an advanced approach which uses artificial intelligence in plant disease detection. This literature discusses briefly about the features of above-mentioned spectroscopy techniques which may impel the general understanding and propel the research activities.
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Tummers, Anna, Arie Wallert, Robert G. Erdmann, et al. "The New York Malle Babbe: Original, Studio Work, or Forgery?*." In Frans Hals. Amsterdam University Press, 2024. https://doi.org/10.5117/9789048566068_ch08.

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Quite a few of the most innovative and best-known paintings by Frans Hals exist in several variants. Attributing some of these versions or imitations is a notoriously difficult challenge. A case in point is the Malle Babbe painting at the Metropolitan Museum in New York, which has a rich attribution history: it has been called an original Frans Hals, a work by one of his sons, an early imitation with a forged signature, and even a modern forgery. This case study sheds new light on its attribution by comparing its style, technique and materials in depth to the well-known original by Frans Hals in Berlin and to the Malle Babbe forgery created by Han van Meegeren at the Rijksmuseum in Amsterdam, and by relating it to relevant primary sources and seventeenth-century art theory. New technical research was done on all three paintings specifically for this study, including infrared reflectography (IRR), macro X-ray fluorescence scanning (MA-XRF), hyperspectral imaging or reflectance imaging spectroscopy (HI/RIS) and lead isotope analysis. Advanced digital tools were developed to aid the comparison.
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Conference papers on the topic "Fluorescence hyperspectral imaging (fHSI)"

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Shupletsov, V. V., I. A. Goryunov, E. V. Potapova, and V. V. Dremin. "Development of Dual-Mode Hyperspectral/Fluorescence Lifetime Imaging System." In 2024 International Conference Laser Optics (ICLO). IEEE, 2024. http://dx.doi.org/10.1109/iclo59702.2024.10624478.

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Abbey, Emma, Oren Katz, Travis Ferguson, Sarah-Johanna Klose, Chris Pruefert, and Hans-Peter Loock. "Applications of Hadamard-Multiplexed Fluorescence Imaging using a Hyperspectral Camera." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.am4a.2.

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We present a hyperspectral fluorescence imaging system based on a Hadamard-multiplexed light source for monitoring spatially inhomogeneous reactions through time and correctly identifying fluorescent components in complex mixtures.
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Bethge, Hans, Inga Weisheit, Mauritz Dortmund, et al. "Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data." In Photonic Technologies in Plant and Agricultural Science II, edited by Dag Heinemann and Gerrit Polder. SPIE, 2025. https://doi.org/10.1117/12.3043114.

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Yu, Woo Hyeong, Changyeun Mo, Hong Gu Lee, Moon S. Kim, Ahyeong Lee, and Ji Hye Choi. "Deep learning model for maturity assessment of Korean melons using hyperspectral fluorescence imaging." In Sensing for Agriculture and Food Quality and Safety XVII, edited by Moon S. Kim, Byoung-Kwan Cho, and Fartash Vasefi. SPIE, 2025. https://doi.org/10.1117/12.3055115.

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Haaland, David M., Howland D. T. Jones, Michael B. Sinclair, et al. "Hyperspectral confocal fluorescence imaging of cells." In Optics East 2007, edited by Christopher D. Brown, Mark A. Druy, and John P. Coates. SPIE, 2007. http://dx.doi.org/10.1117/12.738152.

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Lange, Holger, Ross Baker, Johan Hakansson, and Ulf P. Gustafsson. "Reflectance and fluorescence hyperspectral elastic image registration." In Medical Imaging 2004, edited by J. Michael Fitzpatrick and Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.535720.

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Leavesley, Silas, Yanan Jiang, Valery Patsekin, Heidi Hall, Douglas Vizard, and J. Paul Robinson. "Hyperspectral small animal fluorescence imaging: spectral selection imaging." In Biomedical Optics (BiOS) 2008, edited by Fred S. Azar and Xavier Intes. SPIE, 2008. http://dx.doi.org/10.1117/12.763935.

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Martin, Matthew E., Musundi B. Wabuyele, Masoud Panjehpour, Mary N. Phan, Bergein F. Overholt, and Tuan Vo-Dinh. "Hyperspectral fluorescence imaging system for biomedical diagnostics." In Biomedical Optics 2006, edited by Gerald E. Cohn, Warren S. Grundfest, David A. Benaron, and Tuan Vo-Dinh. SPIE, 2006. http://dx.doi.org/10.1117/12.664113.

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Chandler, Lynn L., Ammasi Periasamy, and Andrea Chandler. "Novel snapshot hyperspectral imager for fluorescence imaging." In Multiphoton Microscopy in the Biomedical Sciences XVIII, edited by Ammasi Periasamy, Peter T. So, Xiaoliang S. Xie, and Karsten König. SPIE, 2018. http://dx.doi.org/10.1117/12.2300933.

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Dahan, Maxime. "Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging." In Imaging Systems and Applications. OSA, 2012. http://dx.doi.org/10.1364/isa.2012.im4c.5.

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Reports on the topic "Fluorescence hyperspectral imaging (fHSI)"

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Nieman, Linda T., Michael B. Sinclair, George S. Davidson, et al. 3D optical sectioning with a new hyperspectral confocal fluorescence imaging system. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/902877.

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Timlin, Jerilyn Ann, and Linda T. Nieman. Development and integration of Raman imaging capabilities to Sandia National Laboratories hyperspectral fluorescence imaging instrument. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/875989.

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