Image Analysis Quantification
Image Analysis Quantification - Related Videos
05:31
Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis
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Cited by 4 •
2020
Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.
11:01
Automated Imaging and Analysis for the Quantification of Fluorescently Labeled Macropinosomes
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Cited by 8 •
2021
Automated assays using multi-well microplates are advantageous approaches for identifying pathway regulators by allowing the assessment of a multitude of conditions in a single experiment. Here, we have adapted the well-established macropinosome imaging and quantification protocol to a 96-well microplate format and provide a comprehensive outline for automation using a multi-mode plate reader.
05:25
Quantification of Dendritic Spines Using Confocal Microscopy Imaging
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2025
Source: Gouder, et. al. Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells. J. Vis. Exp. (2015).This video demonstrates the imaging and analysis of dendritic spines in transduced, immunolabeled pyramidal neurons using confocal microscopy. Background noise is reduced, and dendrites are traced by estimating their diameter and length. Automated spine segmentation involves setting parameters for dendrite and spine dimensions...
07:05
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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2022
We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.
05:10
Three-dimensional Quantification of Intestinal Mucus Using Whole-mount Tissue Imaging
0 Views •
2025
This study presents a 3D imaging method using whole-mount intestinal tissues and multi-photon microscopy to quantify secreted mucus, enabling precise volumetric analysis and visualization of mucus dynamics in response to stimuli like carbamoylcholine chloride.
FAQs
Calibration connects image measurements with the scale or reference needed for meaningful comparison, while consistent measurement conditions reduce variation between samples or experiments. Without these controls, apparent differences in area, intensity, shape, or count may reflect changes in analysis conditions rather than biological or engineered effects. Standardization therefore supports reproducible results and more defensible experimental comparisons.
Segmentation separates regions or objects of interest from the rest of a digital image, making later measurements possible. Thresholds determine which visual information is included in those regions, so inconsistent choices can alter object counts, areas, intensities, or shapes. Applying consistent segmentation and thresholding across comparable samples helps ensure that observed differences represent the systems being studied rather than arbitrary image processing decisions.
The analysis can extract area, intensity, shape, and count, depending on the structures or regions being examined. These features provide different views of system behavior: counts describe abundance, area reflects extent, intensity captures visual signal level, and shape characterizes morphology. Considering several features together can reveal changes in organization or performance that a single measurement might miss.
A typical workflow starts with image preprocessing, followed by calibration and segmentation of relevant regions or objects. The selected regions are then measured to extract features such as area, intensity, shape, or count. Keeping thresholds and measurement conditions consistent throughout the workflow allows the resulting data to support reliable comparisons among biological samples, engineered systems, or experimental groups.
In bioengineering, the approach can characterize cells, tissues, biomaterials, microfluidic devices, and tissue-engineered constructs. The relevant measurements depend on the system, such as morphology for cells or tissues, spatial organization for constructs, and material performance for engineered devices or biomaterials. This broad applicability allows visual changes to be converted into comparable data across diverse research models.
Quantitative imaging can reveal changes in morphology, spatial organization, growth, or material performance. These measurements support comparisons between experimental conditions and can contribute to quality control by identifying whether a biological or engineered system meets expected characteristics. The resulting data also support data-driven design, helping connect image-based observations with decisions about tissue-engineered constructs, biomaterials, or devices.