Imaging Parameters
Imaging Parameters - Related Videos
08:01
Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo
0 Views •
Cited by 11 •
2016
A new electron paramagnetic resonance (EPR) method, rapid scan EPR (RS-EPR), is demonstrated for 2D spectral spatial imaging which is superior to the traditional continuous wave (CW) technique and opens new venues for in vivo imaging. Results are demonstrated at 250 MHz, but the technique is applicable at any frequency.
05:22
Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry
0 Views •
2024
Described here is a method that can be used to image five or more fluorescent parameters by immunofluorescent microscopy. An analysis pipeline for extracting single cells from these images and conducting single-cell analysis through flow cytometry-like gating strategies is outlined, which can identify cell subsets in tissue sections.
01:10
Wave Parameters
0 Views •
2023
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
08:27
Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
0 Views •
Cited by 1 •
2024
The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.
08:43
Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids
0 Views •
Cited by 3 •
2024
Here, we describe different multicellular spheroid formation methods to perform follow-up multi-parameter live cell microscopy. Using fluorescence lifetime imaging microscopy (FLIM), cellular autofluorescence, staining dyes, and nanoparticles, the approach for analysis of cell metabolism, hypoxia, and cell death in live three-dimensional (3D) cancer and stem cell-derived spheroids is demonstrated.
FAQs
Increasing settings that improve spatial resolution or image detail can require more acquisition time, while faster capture may reduce the information available for evaluating small features. Engineers therefore adjust parameters according to the required measurement accuracy, feature size, and inspection speed. The goal is not maximum detail in every case, but a balanced combination that produces usable, consistent data.
Contrast helps distinguish features from their surroundings, while signal-to-noise ratio indicates how clearly meaningful image information rises above unwanted variation. Poor contrast or low signal-to-noise ratio can make defects, boundaries, or dimensional features difficult to assess even when an image appears detailed. Controlling both supports more reliable interpretation and improves confidence in engineering measurements.
Magnification changes the apparent size of features in an image, whereas spatial resolution determines how distinctly nearby or small features can be represented. A highly magnified image is not automatically more informative if the system cannot resolve the relevant detail. Engineers should therefore consider magnification together with feature scale and resolution when assessing dimensions, defects, or microscopic structures.
Parameter selection should match the material being examined, the scale of the relevant feature, and the imaging modality used to capture it. Engineers can then adjust resolution, exposure, contrast, magnification, and signal-to-noise ratio to suit the inspection objective. This approach helps balance detail, acquisition time, and data quality rather than applying identical settings to every sample.
A practical workflow begins by identifying the feature or dimension that must be evaluated, followed by selecting settings appropriate to the material, scale, and imaging modality. Engineers then balance image detail against acquisition time and data quality, checking whether contrast and signal-to-noise ratio support interpretation. Consistent control of the selected settings improves repeatability across measurements and inspections.
Controlled imaging parameters support inspection, dimensional analysis, microscopy, nondestructive testing, medical device development, and automated machine-vision systems. In these settings, parameter choices influence whether surfaces, defects, dimensions, or other visual features can be evaluated consistently. Their use helps engineers produce dependable image data for measurements and decisions while maintaining an appropriate balance between detail and capture efficiency.