Bonej Plugin
Bonej 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
Segmentation supplies the organized image data that BoneJ needs to quantify mineralized architecture. The plugin converts the represented structures into measurements including volume, thickness, connectivity, and anisotropy, which describe different aspects of trabecular and cortical form. These numerical outputs make it possible to compare samples or conditions systematically, instead of depending only on visual impressions.
Volume, thickness, connectivity, and anisotropy should be treated as complementary rather than interchangeable outputs. Each describes a different aspect of trabecular or cortical architecture, so a change in one value does not by itself summarize the whole structure. Reviewing the set helps researchers characterize morphology more completely and interpret differences in relation to mechanical function.
BoneJ Plugin handles segmented data in both two and three dimensions, allowing the analysis format to match the available imaging study. Two-dimensional and three-dimensional datasets can therefore be examined within the same ImageJ or Fiji-based framework, while the selected dimensionality remains part of how results are interpreted. This supports comparisons across image-based skeletal investigations.
A typical analysis begins with a segmented two- or three-dimensional image in ImageJ or Fiji. The researcher applies BoneJ tools to obtain selected measurements, such as volume, thickness, connectivity, or anisotropy, then compares those values across samples or experimental conditions. Keeping the workflow within an established image-analysis environment supports consistent processing and reproducible quantitative comparisons.
Microcomputed tomography is a key source of data for BoneJ, although the plugin can also work with other imaging datasets when they provide suitable segmented digital images. The important input is therefore not a particular scanner alone, but image data organized for quantitative analysis. This makes the workflow adaptable across studies that examine mineralized tissue structure.
In environmental studies, BoneJ can help quantify how skeletal architecture differs among experimental conditions or other environmental contexts represented in the imaging dataset. Measurements provide a structural basis for examining adaptation and for relating morphological changes to mechanical function. The same approach also supports broader biomechanics and biomedical investigations when mineralized tissues are compared quantitatively.