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Related Concept Videos

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal crystal...
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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Related Experiment Video

Updated: Jun 19, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Protocol for automated analysis of biological images using Python code.

Benjamin Sacks1, Jiya Mody1, Yiming Huang1

  • 1Department of Neurology, Robert Wood Johnson Medical School, Institute for Neurological Therapeutics at Rutgers, Rutgers Biomedical and Health Sciences, Piscataway, NJ 08854, USA.

STAR Protocols
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This protocol details Python image analysis for biological research, offering an open-source alternative for feature quantification. Scientists can learn to process, segment, and analyze images with minimal coding experience.

Keywords:
BioinformaticsCell BiologyComputer sciencesMicroscopy

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Area of Science:

  • Bioimage analysis
  • Computational biology
  • Scientific imaging

Background:

  • Commercial image analysis software can be expensive and restrictive.
  • There is a growing need for accessible, flexible tools in biological research.
  • Python offers a powerful, open-source platform for scientific computing.

Purpose of the Study:

  • To provide a user-friendly protocol for quantitative image analysis in Python.
  • To empower scientists with limited coding experience to analyze biological images.
  • To demonstrate the flexibility and capabilities of Python for bioimage analysis.

Main Methods:

  • Reading and preparing diverse biological image file types.
  • Image segmentation techniques for feature isolation.
  • Extracting quantitative measurements from segmented images.
  • Data export for further analysis.

Main Results:

  • Demonstration of successful image processing and analysis using Python.
  • Quantification of features in various common biological image types.
  • Generation of exportable data for downstream applications.

Conclusions:

  • Python-based image analysis is a viable and flexible alternative to commercial software.
  • This protocol enables scientists to perform quantitative bioimage analysis with minimal coding.
  • Open-source tools enhance accessibility and reproducibility in scientific research.