20.05.2020 - 06.06.2020 – Online

Online course: Image Analysis Methods for Biologists

Improve your image analysis knowledge and ability to analyse your images

The use of automatic image analysis in the biological sciences has increased significantly in recent years, especially with automated image capture and the rise of phenotyping.

This online course will help improve your understanding of image analysis methods, and improve your practical skills and ability to apply the techniques to your images.

You will explore the process of image acquisition, through to segmenting regions, counting objects and tracking movement. Importantly, we’ll also try to highlight what to watch out for when using different image analysis approaches.


What topics will you cover?

  • Introduction to image analysis
  • Introduction to digital images and image capture
  • Image noise, and noise reduction approaches
  • Using image analysis to measure traits for phenotyping
  • A brief introduction to computer coding
  • A look at a variety of image segmentation approaches
  • Building 3D models from 2D images
  • Using image analysis to monitor changes over time
  • A first introduction to AI-based approaches, such as deep learning
  • Practical experience of using the Fiji image processing/analysis software

What will you achieve?

By the end of the course, you'll be able to...

  • Improve quality of images captured for scientific experiments
  • Develop an understanding of common image analysis techniques and what goes into making an image analysis tool
  • Apply basic image processing methods to images, such as to reduce the effects of noise
  • Discuss the assumptions and challenges involved in using different image analysis approaches
  • Perform a variety of image segmentation approaches
  • Explore the future of image analysis for phenotyping, including a look at AI-based approaches
  • Investigate computer coding, using the Python language


Read more here


EPPN2020 has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731013