Microscopy Image Analysis
Microscopy Image Analysis - Related Videos
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.
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.
09:26
Experimental Screening Protocols, Immunocytochemistry and Microscopy-based Imaging Techniques for Penium margaritaceum
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2025
This report describes the fundamental methods used to culture and experimentally manipulate the unicellular streptophyte alga, Penium margaritaceum. It also provides fundamental protocols of microscopy-based imaging, including live cell labeling with monoclonal antibodies and other fluorescent probes and scanning electron microscopy.
13:28
High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE
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Cited by 218 •
2017
This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.
09:30
Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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Cited by 13 •
2017
We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using Förster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in µManager and data analysis is undertaken in FLIMfit.
FAQs
Calibration establishes the relationship between image data and the measurements drawn from it. By accounting for imaging scale before quantifying structures, analysts can express observations such as size or spatial organization consistently rather than treating raw pixels as directly comparable measurements. This step supports meaningful comparisons among cells, tissues, biomaterials, and engineered constructs.
Segmentation separates regions of interest from the surrounding image, allowing analysis to focus on individual cells, tissue features, biomaterials, or engineered structures. The quality of this separation affects later measurements because size, shape, intensity, and spatial organization are calculated from the selected regions. Reliable segmentation therefore provides the basis for consistent quantitative comparisons.
Feature extraction converts segmented image regions into measurable descriptors, including size, shape, intensity, spatial organization, or movement. Classification then groups or identifies image features using those measurements. Together, these steps move analysis beyond isolated pixel values and help reveal biological or material patterns that may be difficult to distinguish through visual inspection alone.
Noise reduction helps prepare microscopy images for downstream interpretation by limiting image variation that can interfere with segmentation and feature measurement. It is applied as part of image processing before regions and characteristics are quantified. In practice, this can support more consistent estimates of structure, intensity, spatial organization, or movement across the image set.
A typical workflow begins with calibration, followed by noise reduction and segmentation of relevant image regions. Analysts then extract measurable features and may classify the resulting structures or patterns. Organizing these steps as a consistent sequence helps convert images from different microscopy sources into comparable measurements for studying cells, tissues, biomaterials, or engineered constructs.
The approach can be applied to images produced by light, fluorescence, electron, or other microscopes, while the resulting measurements depend on the structures and processes visible in each image. Researchers can quantify features from cells, tissues, biomaterials, and engineered constructs, making the method useful across biological characterization and bioengineering studies.
In bioengineering, quantitative image analysis supports characterization of cell behavior, tissue architecture, and material performance. Automated processing can increase throughput and reduce observer bias, while measurements of size, shape, intensity, spatial organization, or movement provide structured evidence about engineered constructs and their biological or material properties.