The ongoing progress in fluorescence labeling and in microscope instrumentation allows the generation and the imaging of complex biological samples that contain increasing numbers of fluorophores. For the correct quantitative analysis of datasets with multiple fluorescence channels, it is essential that the signals of the different fluorophores are reliably separated. Due to the width of fluorescence spectra, this cannot always be achieved using the fluorescence filters in the microscope. In such cases spectral imaging of the fluorescence data and subsequent linear unmixing allows the separation even of highly overlapping fluorophores into pure signals. In this chapter, the problems of fluorescence cross talk are defined, the concept of spectral imaging and separation by linear unmixing is described, and an overview of the microscope types suitable for spectral imaging are given.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/978-1-60761-847-8_5 | DOI Listing |
Diabet Med
January 2025
School of Medicine, University of Galway, Galway, Ireland.
Aims: To describe the sonographic features of active Charcot neuro-osteoarthropathy (CNO) and assess the potential role of ultrasound in identifying those with active CNO.
Methods: Using a prospective case-series study design we assessed the sonographic features of 14 patients with a diagnosis of diabetes presenting with clinical signs and symptoms suspicious for active CNO. Patients had standard weight-bearing plain X-Ray and, where possible, MRI to evaluate the presence of active CNO.
Sensors (Basel)
January 2025
Department of Optical Engineering, Utsunomiya University, 7-2-1 Yoto, Utsunomiya 321-8585, Japan.
We describe the various steps of a gas imaging algorithm developed for detecting, identifying, and quantifying gas leaks using data from a snapshot infrared spectral imager. The spectral video stream delivered by the hardware allows the system to combine spatial, spectral, and temporal correlations into the gas detection algorithm, which significantly improves its measurement sensitivity in comparison to non-spectral video, and also in comparison to scanning spectral imaging. After describing the special calibration needs of the hardware, we show how to regularize the gas detection/identification for optimal performance, provide example SNR spectral images, and discuss the effects of humidity and absorption nonlinearity on detection and quantification.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Earth, Environment and Geospatial Sciences, Saint Louis University, Saint Louis, MO 63108, USA.
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!