Image Analysis Methods for Biologists

Online Course

Future Learn
Image Analysis Methods for Biologists

What is the course about?

Image Analysis Methods for Biologists
The course Image Analysis Methods for Biologists is an online class provided by The University of Nottingham through Future Learn. It may be possible to receive a verified certification or use the course to prepare for a degree.

Get an introduction to image acquisition and analysis for biologists – from basic techniques to the future of image analysis.

Course description
  • Improve your image analysis knowledge and ability to analyse your images
  • 4 weeks
  • 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.
  • 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
  • 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
  • This course is designed for postgraduate and postdoctoral researchers in biological sciences.
  • 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.
  • Development and delivery of this course is supported by Biotechnology and Biological Sciences Research Council Training Grant BB/P011845/1 Image Analysis for Biologists: An Online Course.
  • The University of Nottingham
  • The University of Nottingham is committed to providing a truly international education, inspiring students with world-leading research and benefitting communities all around the world.

Prerequisites & Facts

Image Analysis Methods for Biologists

Course Topic

Career Development, Personal Development

University, College, Institution

The University of Nottingham

Course Skill Level

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

Degree & Cost

Image Analysis Methods for Biologists

To obtain a verified certificate from Future Learn / The University of Nottingham you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
The University of Nottingham
Image Analysis Methods for Biologists
provided by Future Learn

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School: The University of Nottingham
Topic: Biology and Biotechnology, Science, Engineering & Maths