Open Source Image Processing
Open Source Image Processing - Related Videos
10:48
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
0 Views •
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.
10:04
Flame Experiments at the Advanced Light Source: New Insights into Soot Formation Processes
0 Views •
Cited by 4 •
2014
Gas sampling from laboratory-scale flames with online analysis of all species by mass spectrometry is a powerful method to investigate the complex mixture of chemical compounds occurring during combustion processes. Coupled with tunable soft ionization via synchrotron-generated vacuum-ultraviolet radiation, this technique provides isomer-resolved information and potentially fragment-free mass spectra.
10:41
Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
0 Views •
Cited by 9 •
2015
Here it is shown how to track and quantify developmental processes in C. elegans. The methods presented are based on open-source tools that can be easily implemented. It is demonstrated how to reconstruct 3D cell-shape models, how to manually track subcellular structures, and how to analyze cortical contractile flow.
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
0 Views •
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.
10:12
Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
0 Views •
Cited by 8 •
2021
Detailed protocol and three Python scripts are provided for operating an open-source robotic liquid handling system to perform semi-automated protein sample preparation for mass spectrometry experiments, covering detergent removal, protein digestion, and peptide desalting steps.
FAQs
Calibration prepares image values for quantitative interpretation, filtering transforms the image data, segmentation organizes relevant spatial regions, and feature extraction converts visible characteristics into measurable features. In chemistry, this sequence connects raw microscopy or mapping images with results such as particle size, phase distribution, reaction changes, or material morphology.
Publicly available source code lets researchers inspect how an analysis was implemented, modify the workflow for a specific chemical image, and share the same computational procedure with others. These properties support reproducibility because the processing steps are not confined to an inaccessible software environment. They also lower barriers for teaching and customized research analyses.
Pixel intensity and spatial information provide complementary evidence. Intensity can contribute to distinguishing or quantifying visual features, while spatial information supports measurements of arrangement, size, and distribution. After processing, these data can describe particle size, phase distribution, reaction changes, or material morphology, turning chemical images into quantitative evidence rather than visual impressions alone.
Customized workflows are useful when chemical images differ in content or purpose. Researchers can adapt the processing sequence to microscopy images, chemical maps, or other experimental visuals, then select measurements suited to particles, phases, reactions, or material structure. This flexibility allows one open workflow to address varied research questions without restricting analysis to a fixed procedure.
Begin with the experimental image, then apply calibration, filtering, segmentation, and feature extraction in a deliberate sequence. The processed output can quantify a selected characteristic, such as particle size or phase distribution, or track reaction changes. Keeping the workflow inspectable and shareable helps other researchers understand how the image became the reported measurement.
Chemists can apply these methods to microscopy images, chemical maps, and visual data from experiments. Typical outcomes include measurements of particle size, phase distribution, reaction changes, and material morphology. The same approach also supports research and teaching, giving learners and investigators a way to practice quantitative imaging with tools that can be examined, modified, and shared.