Imagej Tracking
Imagej Tracking - Related Videos
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.
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.
05:55
Multiple Object Tracking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University In a staggeringly complex and engaging world, it is crucial to selectively process some stimuli at the expense of others. Experimental psychologists call this ability attention. Specifically, visual attention refers to the ability to selectively process aspects of a visual scene. Many paradigms used to study visual attention involve brief, punctuated, and repeated trials. However, everyday situations often place sustained demands...
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.
FAQs
Object detection depends on measurable image features, including intensity, size, and shape. These criteria help distinguish the cells, particles, or other structures of interest from surrounding image content in each microscopy frame. Selecting features that match the target object supports more consistent detection, which is essential for reconstructing trajectories and comparing movement across experimental conditions.
The method links detections observed in sequential frames so that corresponding objects can be followed over time. This frame-to-frame association produces trajectories rather than isolated measurements, allowing the analysis to represent continuous movement. Reliable linking is important because calculated displacement, speed, and directionality depend on how consistently each object is matched across the image sequence.
ImageJ Tracking can quantify speed, displacement, and directionality from reconstructed object trajectories. Speed describes how rapidly an object moves, displacement captures change in position, and directionality indicates how consistently movement follows a particular path. Together, these measures help distinguish changes in cellular or particle motility rather than relying only on visual inspection of time-lapse images.
A basic workflow begins with sequential microscopy images, identifies the objects of interest using features such as intensity, size, or shape, and links those detections across frames. The resulting trajectories can then be used to calculate movement measures, including speed, displacement, and directionality. This converts a time-lapse sequence into quantitative data suitable for comparing dynamic behavior.
In immunology and infection research, ImageJ Tracking can examine immune-cell migration, pathogen movement, and cell-cell interactions. It is particularly useful when the biological question concerns behavior over time rather than a single image. Quantified trajectories allow investigators to characterize motility and assess how dynamic cellular or pathogen behavior changes during inflammation or antimicrobial responses.
Tracking data can reveal changes in speed, displacement, and directionality between experimental conditions. Researchers can use these measurements to compare cellular behavior when infection, signaling, or treatment alters motility. Because the analysis converts microscopy observations into reproducible quantitative data, it supports structured evaluation of dynamic responses and helps relate altered movement to immune or antimicrobial processes.