Roi Measurement
Roi Measurement - Related Videos
10:53
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
0 Views •
2019
The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.
14:39
Measuring Phagosome pH by Ratiometric Fluorescence Microscopy
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Cited by 26 •
2015
Phagosomal pH influences phagosome maturation, oxidant production, phagosomal killing as well as antigen presentation. Here we describe a ratiometric method for measuring time-course and endpoint pH changes in individual phagosomes in living phagocytes using fluorescence microscopy.
07:22
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
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Cited by 7 •
2017
Optimized sampling protocols and the development of new wipe materials can be facilitated by standardized measurements of collection efficiency from wipe-sampling. Our approach for sampling trace explosives uses an automated device to control speed, force, and distance during wipe-sampling followed by extraction of collected explosives.
06:56
Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes
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Cited by 7 •
2017
A sampling moiré technique featuring 2-pixel and multi-pixel sampling methods for high-accuracy strain distribution measurements at the micro/nano-scale is presented here.
11:22
Measuring the Kinetics of mRNA Transcription in Single Living Cells
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Cited by 6 •
2011
RNA polymerase II transcriptional kinetics are measured on specific genes in living cells. mRNAs transcribed from the gene of interest are fluorescently tagged and using Fluorescence Recovery After Photobleaching (FRAP) the in vivo kinetics of transcriptional elongation are obtained.
FAQs
The boundary drawn around a cell, tissue, or signal determines which image information contributes to the result. If the outline includes neighboring structures, measured area, intensity, shape, or fluorescence may no longer represent the intended target. Consistent boundaries across samples therefore matter as much as the calculation itself, particularly when comparing biological conditions or treatments.
Background and control regions provide reference values for interpreting measurements from the target ROI. Comparing a signal with these references helps distinguish the feature of interest from surrounding image signal or baseline conditions. This approach is especially useful for fluorescence and protein-expression analysis, where relative differences can support more meaningful comparisons between biological samples.
Reproducibility depends on applying the same ROI-selection approach and measurement settings throughout an experiment. Differences in how regions are outlined, segmented, or quantified can introduce variation that reflects analysis rather than biology. Standardized procedures make measurements more comparable across images and improve the reliability of numerical data used for statistical analysis.
Different measured properties describe different biological features. Area can indicate the extent of a structure, shape can support morphological characterization, and intensity or fluorescence can represent signal differences within selected regions. Examining the property relevant to the research question allows image data to contribute more directly to cell, tissue, or protein-expression analysis.
A typical workflow begins by selecting the biological feature to analyze, such as a cell, tissue region, structure, or signal. The researcher then outlines the region manually or uses image-segmentation software, applies the chosen measurement settings, and calculates relevant properties. Reference background or control regions can be included before organizing results for statistical analysis.
This approach is useful when researchers need quantitative information from microscopy or other biological images rather than visual descriptions alone. Applications supported by the method include cell counting, protein-expression analysis, tissue characterization, and evaluating experimental treatments. Converting selected image regions into numerical measurements helps connect observed structures or signals with statistical comparisons.