Imagej Segmentation
Imagej Segmentation - Related Videos
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.
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.
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.
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.
03:24
Anterior Eye Segment Dissection: A Method to Isolate Cornea Bearing Anterior Segment from Porcine Eye
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2025
In this video, we demonstrate the dissection of the cornea-bearing anterior segment of the porcine eye. The isolated segment can be used for organ culturing to study the pathophysiology of eye disorders.
FAQs
Pixel classification depends on the visual information used to distinguish a target from surrounding regions. Grayscale intensity can separate areas with different brightness, while color information may help distinguish structures that appear similar in brightness but differ chromatically. Choosing the appropriate representation affects which pixels enter the analysis and therefore influences the reliability of measurements from cells, tissues, particles, or materials.
Thresholding assigns pixels to selected classes according to their intensity or color, creating a basis for separating the target from background. The resulting binary mask provides a defined representation of the selected region for later analysis. Because this mask determines which pixels contribute to measurements, its quality directly affects estimates of structure and properties.
Initial pixel classification may include unwanted noise or combine objects that touch one another. ImageJ workflows can therefore apply processing steps to remove irrelevant regions, separate connected objects, or refine regions of interest. These operations help the mask correspond more closely to the structures being studied, improving measurements of features such as cell number, morphology, or particle characteristics.
Validation reveals whether the selected pixels and processing steps represent the structures visible in the source image. A mask can appear technically complete while excluding relevant regions, including background, or merging neighboring structures. Comparing results with the original image supports reproducible analysis and helps identify errors before measurements are used for phenotype analysis, material characterization, or modeling.
A typical workflow begins by selecting relevant grayscale or color information, applying thresholding to classify pixels, and converting the classification into a binary mask. Processing can then remove noise, separate touching objects, or define regions of interest. The final regions are measured and checked against the source image so that the resulting values remain connected to the observed structures.
Bioengineers apply it when images must support quantitative comparisons rather than description alone. The workflow can help assess cell number and morphology, vessel networks, scaffold features, particles, tissues, or other material structures. Converting visual patterns into measurements supports phenotype analysis, biomaterial characterization, and quality control across microscopy and material-image datasets.
Segmented regions can provide measurements of structure and properties that would be difficult to compare consistently from images alone. Depending on the target, these outcomes may describe cell number, morphology, vessel-network features, scaffold characteristics, or particle-related structure. Such measurements can support quality control and computational modeling, provided the masks have been validated against the original images.