Image J Analysis
Image J Analysis - Related Videos
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
07:46
Optical Materialography Part 2: Image Analysis
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2023
Source: Faisal Alamgir, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA The imaging of microscopic structures of solid materials, and the analysis of the structural components imaged, is known as materialography. Often, we would like to quantify the internal three-dimensional microstructure of a material using only the structural features evidenced by an exposed two-dimensional surface. While X-ray based tomographical methods can reveal buried...
10:19
Analysis and Imaging of Osteocytes
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Cited by 1 •
2024
This study outlines the method to visualize and develop three-dimensional (3D) models of osteocytes within the lacunar-canalicular network (LCN) for computational fluid dynamics (CFD) analysis. The generated models using this method help to understand osteocyte mechanosensation in healthy or diseased bones.
FAQs
Scale calibration links pixel dimensions to the physical scale represented in an image, allowing measurements such as area and length to be reported meaningfully rather than only as pixel counts. The calibration must be applied consistently across comparable images; otherwise, apparent differences may reflect image scaling instead of biological variation.
Thresholding selects pixels according to their intensity or color, while segmentation uses that selection to separate structures or regions for measurement. The chosen threshold can therefore influence which pixels are included in an object and alter calculated area, shape, or fluorescence intensity. Applying the same decision rules across samples helps make comparisons more objective.
Region-of-interest selection focuses measurement on a defined cell, tissue area, or other structure instead of the entire image. This step is especially important when several biological features appear together, because background or unrelated regions can otherwise affect the result. Recording consistent ROI rules supports reproducibility and makes measurements from different samples more comparable.
Pixel intensity and color data can be used to evaluate fluorescence intensity and spatial localization, including where a protein signal appears within a biological sample. Interpretation depends on analyzing corresponding regions consistently, rather than treating a visually brighter image as sufficient evidence. Quantitative measurements can then support comparisons of localization or signal levels across experimental samples.
A practical workflow begins by calibrating the image scale, selecting an appropriate threshold or segmentation approach, defining regions of interest, and measuring features such as area, length, shape, or intensity. The resulting values can be organized across samples for statistical comparison. Keeping these steps standardized limits subjective interpretation and strengthens reproducibility.
It can quantify cell morphology, tissue organization, protein localization, and growth patterns from microscopy-based experiments. The same measurement framework supports work in cell biology, histology, and developmental research, where investigators need objective values rather than descriptive observations alone. Comparing measurements statistically helps evaluate whether observed differences are consistent across samples.