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Imaging Biological Samples with Optical Microscopy

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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: May 19, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

A computational image analysis glossary for biologists.

Adrienne H K Roeder¹, Alexandre Cunha, Michael C Burl

¹Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. aroeder@cornell.edu

Development (Cambridge, England)

|August 9, 2012

Summary

This summary is machine-generated.

Biological imaging generates vast datasets, requiring computational tools for objective analysis. This primer introduces image analysis terms and highlights their importance in developmental biology research.

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Area of Science:

Developmental biology
Biological imaging
Computational analysis

Background:

Digital biological imaging generates large, complex image datasets.
Traditional manual image analysis is insufficient for large datasets.
High-throughput objective analysis tools are needed to extract quantitative information.

Framework:

Collaborative development of analysis tools by biologists, computer scientists, mathematicians, and physicists.
Focus on computational methods for quantitative image analysis.
Importance of robust image analysis in developmental studies.

Implementation:

Presenting a glossary of image analysis terms for biologists.
Discussing the necessity of objective, high-throughput analysis.
Bridging the gap between biological data and computational tools.

Implications:

Enables efficient and accurate analysis of large biological image datasets.
Facilitates deeper understanding of developmental processes.
Promotes interdisciplinary collaboration in scientific research.