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Fluorescence Imaging and Sensing
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Fluorescence Imaging and Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 6840

Special Issue Editors

Prof. Dr. Cheng Lei

E-Mail Website
Guest Editor
Institute of Industrial Sciences, Wuhan University, Wuhan 430072, China
Interests: image
Dr. Marco Chino

E-Mail Website
Guest Editor
Department of Chemical Sciences, University of Naples “Federico II”, 80100 Naples, Italy
Interests: protein-protein interactions; colorimetric immunosensors; bioinorganic oxidations; protein design; protein chromatography; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the Nobel Prize in Chemistry was awarded to three physicists in 2014 for their contributions to the development of super-resolution fluorescence microscopy, a sharp rise in fluorescence imaging-related publications has been witnessed in various journals. In particular, with the invention of novel laser sources, sensors, system architectures, and image-analyzing methods, fluorescence imaging is now a routine diagnostic method in clinical settings, and enhances research in life sciences, such as cell screening, tissue measurement, drug discovery, and ion/molecule sensing. Here, we propose a Special Issue to highlight “Fluorescence Imaging and Sensing”, aiming to provide a valuable forum where researchers can share their most recent advanced techniques, high-performance components, and applications of fluorescence imaging.

Topics covered may include, but are not limited to:

  • Sensors for fluorescence signal detection;
  • Laser sources for fluorescence excitation;
  • Components for fluorescence signal processing;
  • Methods for fluorescence imaging/detection;
  • Applications of fluorescence imaging/detection;
  • Algorithms for fluorescence image analysis.

Prof. Dr. Cheng Lei
Dr. Marco Chino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

16 pages, 1373 KiB  
Article
Distortion Correction and Denoising of Light Sheet Fluorescence Images
by Adrien Julia, Rabah Iguernaissi, François J. Michel, Valéry Matarazzo and Djamal Merad
Sensors 2024, 24(7), 2053; https://doi.org/10.3390/s24072053 - 23 Mar 2024
Viewed by 517
Abstract
Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. [...] Read more.
Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Sensing)
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18 pages, 15372 KiB  
Article
Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method
by Weize Shi and Yabin Wang
Sensors 2023, 23(19), 8264; https://doi.org/10.3390/s23198264 - 06 Oct 2023
Viewed by 736
Abstract
Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace [...] Read more.
Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW–Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Sensing)
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7 pages, 1397 KiB  
Communication
Constructing an In Vitro and In Vivo Flow Cytometry by Fast Line Scanning of Confocal Microscopy
by Xiaohui Zhao, Leqi Ding, Jingsheng Yan, Jin Xu and Hao He
Sensors 2023, 23(6), 3305; https://doi.org/10.3390/s23063305 - 21 Mar 2023
Viewed by 1478
Abstract
Composed of a fluidic and an optical system, flow cytometry has been widely used for biosensing. The fluidic flow enables its automatic high-throughput sample loading and sorting while the optical system works for molecular detection by fluorescence for micron-level cells and particles. This [...] Read more.
Composed of a fluidic and an optical system, flow cytometry has been widely used for biosensing. The fluidic flow enables its automatic high-throughput sample loading and sorting while the optical system works for molecular detection by fluorescence for micron-level cells and particles. This technology is quite powerful and highly developed; however, it requires a sample in the form of a suspension and thus only works in vitro. In this study, we report a simple scheme to construct a flow cytometry based on a confocal microscope without any modifications. We demonstrate that line scanning of microscopy can effectively excite fluorescence of flowing microbeads or cells in a capillary tube in vitro and in blood vessels of live mice in vivo. This method can resolve microbeads at several microns and the results are comparable to a classic flow cytometer. The absolute diameter of flowing samples can be indicated directly. The sampling limitations and variations of this method is carefully analyzed. This scheme can be easily accomplished by any commercial confocal microscope systems, expands the function of them, and is of promising potential for simultaneous confocal microscopy and in vivo detection of cells in blood vessels of live animals by a single system. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Sensing)
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12 pages, 3676 KiB  
Communication
Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks
by Kaihua Wei, Bojian Chen, Zejian Li, Dongmei Chen, Guangyu Liu, Hongze Lin and Baihua Zhang
Sensors 2022, 22(20), 7764; https://doi.org/10.3390/s22207764 - 13 Oct 2022
Cited by 6 | Viewed by 1825
Abstract
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence [...] Read more.
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Sensing)
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15 pages, 4359 KiB  
Article
Pixel Image Analysis and Its Application with an Alcohol-Based Liquid Scintillator for Particle Therapy
by Ji-Won Choi, Ji-Young Choi, Hanil Jang, Kyung-Kwang Joo and Byoung-Chan Kim
Sensors 2022, 22(13), 4876; https://doi.org/10.3390/s22134876 - 28 Jun 2022
Viewed by 1367
Abstract
We synthesized an alcohol-based liquid scintillator (AbLS), and we implemented an auxiliary monitoring system with short calibration intervals using AbLS for particle therapy. The commercial liquid scintillator used in previous studies did not allow the user to control the chemical ratio and its [...] Read more.
We synthesized an alcohol-based liquid scintillator (AbLS), and we implemented an auxiliary monitoring system with short calibration intervals using AbLS for particle therapy. The commercial liquid scintillator used in previous studies did not allow the user to control the chemical ratio and its composition. In our study, the chemical ratio of AbLS was freely controlled by simultaneously mixing water and alcohol. To make an equivalent substance to the human body, 2-ethoxyethanol was used. There was no significant difference between AbLS and water in areal density. As an application of AbLS, the range was measured with AbLS using an electron beam in an image analysis that combined AbLS and a digital phone camera. Given a range–energy relationship for the electron expressed as areal density, the electron beam range (cm) in water can be easily estimated. To date, no literature report for the direct comparison of a pixel image analysis and Monte Carlo (MC) simulation has been published. Furthermore, optical tomography of the inverse problem was performed with AbLS and a mobile phone camera. Analyses of optical tomography images provide deeper insight into Radon transformation. In addition, the human phantom, which is difficult to compose with semiconductor diodes, was easily implemented as an image acquisition and analysis system. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Sensing)
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