Image Analysis Protocol
Image Analysis Protocol - Related Videos
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.
10:25
Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy
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Cited by 15 •
2011
The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.
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.
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.
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.
FAQs
Calibration anchors image measurements to a consistent scale or reference, so differences in area, intensity, shape, or spatial distribution can be compared fairly. This matters when images come from different samples or experimental conditions, because apparent variation should reflect biological differences rather than inconsistent measurement. It also strengthens reproducibility across analyses.
Preprocessing reduces image noise before segmentation, making cells or subcellular structures easier to distinguish from surrounding signal. Segmentation then assigns image regions to the objects being measured. If either stage is inconsistent, later values for area, intensity, or shape may not be comparable. Standardizing both stages helps ensure differences reflect specimens rather than processing choices.
Feature selection should match the neuroscience question. Area and shape can describe neuronal morphology, intensity can quantify signal from markers, and spatial distribution can reveal how structures or activity patterns are arranged. Measuring every available feature is not automatically useful; choosing biologically relevant outputs makes statistical comparisons easier to interpret and connects image data with the experimental question.
Unlike a purely visual assessment, quantitative analysis converts observed patterns into measurements that can be compared statistically. This reduces dependence on subjective judgments and allows researchers to evaluate whether differences in neuronal structures, markers, regions, or activity patterns are consistent across conditions. The result is a clearer connection between microscopy findings and changes associated with development, disease, behavior, or treatment.
A practical workflow begins by calibrating the images, then reducing noise, segmenting the relevant cells or structures, and extracting selected measurements. Researchers should apply the same logic across comparison groups so that processing does not vary with experimental condition. The resulting dataset can then support statistical comparisons of area, intensity, shape, or spatial distribution.
Neuroscientists can apply the protocol to quantify neuronal morphology, synaptic markers, brain regions, or patterns of neural activity. The same general logic supports comparisons linked to development, disease, behavior, and responses to experimental treatments. Its value is not limited to producing images; it provides measurable outcomes that help relate visual changes to specific experimental conditions.