Imagej Analysis
Imagej Analysis - Related Videos
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
11:01
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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Cited by 90 •
2016
This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.
07:19
Clock Scan Protocol for Image Analysis: ImageJ Plugins
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Cited by 16 •
2017
This paper describes two novel ImageJ plugins for 'Clock Scan' image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.
09:39
TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis
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Cited by 3 •
2022
TrackMate Analysis of Calcium Imaging (TACI) is an open-source ImageJ plugin for 3D calcium imaging analysis that examines motion on the z-axis and identifies the maximum value of each z-stack to represent a cell's intensity at the corresponding time point. It can separate neurons overlapping in the lateral (x/y) direction but on different z-planes.
08:44
Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ
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Cited by 35 •
2018
We have developed a simple and adaptable workflow to extract quantitative data from fluorescence-imaging-based cell biological studies of protein aggregation and autophagic flux in the central nervous system of Drosophila models of neurodegeneration.
FAQs
Background subtraction separates image signal from unwanted background information before measurements are made. This preprocessing step can make structures or fluorescence signals easier to distinguish, supporting later thresholding, segmentation, and intensity calculations. In biological images, reducing background contribution helps measurements such as area, signal intensity, or particle number reflect the specimen more consistently rather than variation in the surrounding image.
Thresholding identifies pixels that meet selected image criteria, while segmentation organizes the selected pixels into measurable structures or regions. Together, these operations determine which parts of an image contribute to counts, areas, or intensity values. Their use is especially important when analyzing cells, particles, or tissue features because measurement outcomes depend on how clearly relevant structures are separated from other image content.
A region of interest, or ROI, defines the specific portion of an image included in a measurement. Restricting analysis to selected cells, tissue areas, or other structures allows ImageJ to calculate features such as area and intensity for defined locations rather than for the entire image. This focused approach supports comparisons among biological regions and reduces the influence of unrelated image content.
A typical workflow begins with image processing, such as background subtraction, followed by thresholding or segmentation to identify relevant structures. The user then defines regions of interest and applies measurement tools to obtain values such as area, intensity, or particle number. Keeping these steps consistent across images creates a reproducible analysis and supports comparison of biological samples or experimental conditions.
Plugins extend the available ImageJ functions so researchers can adapt workflows to different imaging modalities and analytical needs. They are useful when standard processing or measurement tools do not match a particular image type or biological question. Combined with documented, repeatable workflows, plugins allow specialized analyses while preserving the broader goal of extracting consistent quantitative information from images.
The approach is useful when microscopy images must be converted into quantitative measurements rather than assessed only by visual inspection. Biological researchers can apply it to cell counting, fluorescence quantification, tissue morphometry, and evaluation of experimental phenotypes. These applications support analysis across samples and can reduce subjective visual assessment by providing numerical features for comparison.
ImageJ analysis can produce measurements including area, intensity, and particle number, which serve as quantitative indicators of biological structure or signal. In microscopy studies, these values can be used to describe cell abundance, fluorescence levels, tissue form, or phenotypic differences between experimental conditions. The resulting measurements provide a more consistent basis for evaluating image-based biological observations.