Objectj Plugin
Objectj Plugin - 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.
08:14
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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Cited by 93 •
2013
Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.
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.
09:36
Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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2018
We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone's outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.
FAQs
The plugin associates each user-defined annotation with a discrete object in the digital image, allowing observations and measurements to remain connected to their visual location. An annotation may be a point, outline, or region, while the resulting values are recorded in structured tables. This connection helps researchers trace numerical results back to the biological feature that produced them.
Different annotation forms capture different kinds of visual information. Points can mark discrete locations, outlines can represent object boundaries, and regions can identify spatial areas within an image. Choosing an annotation that matches the feature being studied helps organize observations consistently and supports measurements of cells, tissues, organisms, or other experimental features.
Reproducibility improves when researchers record observations through a consistent annotation and measurement structure rather than relying only on informal visual descriptions. ObjectJ Plugin keeps annotations linked to image data and places measurements in organized results tables. This creates a documented record that can support repeated analysis, comparison across images, and clearer communication of how visual findings were quantified.
Structured results tables turn separate image observations into an organized dataset in which measurements can be reviewed by object. Researchers can use these records to compare groups, examine variation among biological features, and relate numerical measurements to their corresponding image annotations. The tabular format also makes the analysis easier to document than relying on unstructured notes.
A typical workflow begins by opening digital image data, identifying the objects or features of interest, and applying suitable user-defined annotations such as points, outlines, or regions. Measurements are then recorded for each annotated object in results tables. Researchers can organize those records across images and use them to compare observations or describe spatial and morphological patterns.
The plugin is useful when a study requires repeatable marking, measurement, and organization of discrete image objects without building every analysis step through custom code. It can support practical quantification of cells, tissues, organisms, or experimental features while retaining links to the source images. This makes it suitable for workflows where transparent documentation and structured comparison are important.
Depending on the image and annotation strategy, analyses can quantify biological objects and experimental features while preserving their spatial or morphological context. Researchers may use the resulting records to compare groups across microscopy images, assess patterns in object shape or location, and organize observations for further interpretation. The plugin therefore connects visual evidence with measurable biological data.