An Image Analysis Success Story

This post originally appeared on the Software Carpentry website.

I am a alumni of the bootcamp in Paris in June 2012. I have been working on a paper on digital image analysis for experimental studies in ecology and evolution working with small-scale model organisms such as aquatic microbes, insects or fish. Besides explaining the principles of image analysis, we provide scripts in Python, R and ImageJ so people can directly use the methods. The paper has now been published, and I'd like to share our success with the Software Carpentry community.

Implementing image analysis in laboratory-based experimental systems for ecology and evolution: a hands-on guide

The paper explains the basic ideas behind digital image analysis such as different approaches for image segmentation and how to create an automated and validated workflow to extract object counts and morphology measurements on thousands of images. This facilitates quite a bit the work of biologists which otherwise spend hours counting and measuring objects manually on a microscope. Additionally, we provide scripts written in Python, R and the macro language of ImageJ so everyone can embrace the presented methods and perform image analysis on a set of test images (and then on his own set of images). A Software Carpentry bootcamp introduced me to Python programming in general and some of the libraries used (scikits-image) and I also enjoyed quite a bit of your tutorials which helped me to write the scripts.

Counting and Measuring Cells
Overlay of One Test Image to Count and Measure Tetrahymena thermophila Cells

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