Open Source Image Analysis
Open Source Image Analysis - Related Videos
10:48
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
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Cited by 27 •
2013
This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.
09:30
Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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Cited by 14 •
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.
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
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Cited by 8 •
2011
The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.
09:32
Cortical Source Analysis of High-Density EEG Recordings in Children
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Cited by 6 •
2014
In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.
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.
FAQs
The workflow progressively transforms raw pixel data into interpretable measurements. Image enhancement can improve the visibility of relevant structures, thresholding separates regions according to image characteristics, and segmentation defines individual objects or areas for analysis. Object detection and feature extraction then identify targets and quantify properties, allowing researchers to move from visual images to measurements of cells, tissues, biomaterials, or engineered structures.
Segmentation establishes which pixels belong to a structure of interest and which belong to the surrounding image. This separation supports later measurements and helps distinguish individual cells, tissue regions, biomaterials, or engineered features. Because the resulting measurements depend on how image regions are defined, segmentation is a central link between visual data and quantitative interpretation in bioengineering studies.
Freely available software, algorithms, and source code can reduce access barriers while allowing researchers to inspect, adapt, and reuse analysis workflows. That flexibility is useful when standard tools do not match a specialized experiment. Open workflows also support transparent research, making it easier to compare how image data were processed across laboratories rather than treating software access as a limitation.
Enhancement, thresholding, segmentation, object detection, and feature extraction each influence how image content becomes quantitative information. For example, defining regions during segmentation determines which pixels contribute to later measurements, while detection and feature extraction determine which structures and properties are recorded. Careful selection of these processing stages is therefore important when comparing cells, tissues, biomaterials, or engineered structures.
A practical workflow begins with digital image data, followed by image enhancement when relevant structures need clearer representation. Researchers can then apply thresholding and segmentation, detect objects or regions of interest, and extract features for quantitative interpretation. The resulting measurements can characterize biological or engineered structures and support transparent, reproducible analysis when the same workflow is applied across datasets.
The approach applies to microscopy and other digital imaging data used to study cells, tissues, biomaterials, and engineered structures. Its processing stages can identify regions, objects, and measurable features within those images. This breadth makes the method relevant both to biological characterization and to evaluating structures created or studied through bioengineering research.
Researchers may choose it when they need quantitative information from microscopy or imaging data and want software that can be customized for a specialized experiment. It is useful for characterizing cells and tissues as well as biomaterials and engineered structures. The accessible nature of these tools can also help laboratories with different resources participate in comparable image-based measurements.
Using accessible software, algorithms, and source code can make image-processing procedures more transparent and reproducible. Researchers can apply comparable measurement approaches to imaging data from different laboratories, which supports clearer comparison of results. In bioengineering, this can strengthen the interpretation of measurements describing cells, tissues, biomaterials, or engineered structures without limiting analysis to a single software environment.