• University of Nottingham

Image Analysis Methods for Biologists

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

3,132 enrolled on this 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.

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Skip to 0 minutes and 5 seconds This course is about image analysis methods for biologists. Image analysis is a branch of computer science in which we’re concerned with taking digital images of the world and extracting, from those images, some kind of quantitative data that describes the objects and the things we see. With the recent increase in demand for plant phenotyping, especially automated plant phenotyping using robotics, there’s an increased interest and a need to measure things automatically in these images.

Skip to 0 minutes and 34 seconds And so this course is designed to give you an introduction to things you need to think about when you’re capturing images and how to start to go about analysing them and looking at some of the techniques that you can use to start to get at the interesting data in your images. Why are we doing this for biologists? Well, images are everywhere in biology these days. Biologists typically use colour cameras, microscopes to look at populations of cells dividing and growing, specialist devices like confocal laser microscopes to make three dimensional images of the structure of the samples, and more recently, things like microcomputer tomography, x-ray machines, and magnetic resonance imaging to look at the 3D structure of larger objects.

Skip to 1 minute and 17 seconds We’ll look at some of the things to think about during image acquisition, how do take the best quality images, what to do with your images if they are still affected by things such as image noise, reducing the overall quality of the image. And then once you’ve got those images, you want to do things like identifying which pixels belong to plants. It is possible to take these images and manually mark them up to have a user look at them and point at the points of interest and make measurements by hand. The difficulty with that is that the users tend to get very tired, very quickly.

Skip to 1 minute and 51 seconds There tends to be little variation between the measurements produced by different people, and overall, the data that you produce is quite subjective. It also takes a very long time to do. What image analysis methods can do is provide automated methods, in the form of software tools, that can take an input image and automatically objectively produce accurate quantitative data with a minimum of human intervention. And so this course should give you a good overview of some common techniques that you will use, perhaps where the future is heading as well, and we’ll try to give you some pointers to some more advanced topics, as well as covering the basics.

Skip to 2 minutes and 30 seconds We don’t assume that the people watching these videos are computer scientists or have any prior knowledge of image analysis and computer vision. We’re not aiming for a very detailed understanding of the techniques, just enough to allow you to exploit the methods that are already there.

Syllabus

  • Week 1

    Introduction to images and image analysis

    • The image analysis problem

      An overview of image analysis: what we want to achieve, and why it is hard

    • Noise, and noise reduction

      The quality of images is of key importance if they are to be used for further analysis. Here we take a first look at some of the processes which can degrade the quality of images, such as image noise.

  • Week 2

    Measuring from images

    • Measuring for phenotyping

      Phenotyping has risen in popularity. Here we consider some options for measuring phenotyping data from images.

    • An introduction to coding

      Underlying image analysis, and often required for automatic measurements, computer coding allows us to write programs or scripts. Here we provide an introduction to the topic.

  • Week 3

    Segmentation: labelling regions in images

    • Pixel-based segmentation

      An introduction to the lowest level of segmentation, working with individual pixels

    • Region-based segmentation

      Collecting pixels together allows us to make more informed segmentation of images.

    • Model-based segmention

      Here we are starting to use mathematical models of shape and colour to help us segment objects in images

  • Week 4

    What next?

    • 3D models from 2D images

      Here we introduce the concept of a 3D model, and talk about how to generate one from a set of 2D images.

    • Motion and growth

      Sometimes we wish to monitor something changing or growing over time. This requires a special set of image analysis techniques, which we will introduce here.

    • AI based approaches: Deep learning

      Recently, huge advances have been made in the kind of images which can be analysed. One of the more major advances is Deep Learning. Will will introduce the topic here, overview how it works, and talk about future potential.

When would you like to start?

Start straight away and learn at your own pace. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

You can take this self-guided course and learn at your own pace. On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

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

Who is the course for?

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.

What software or tools do you need?

The course and practicals refer to the open-source Fiji software (http://fiji.sc/). To use this you will need a computer (rather than a tablet or smartphone). Please see installation instructions at the Fiji website.

Who will you learn with?

Andrew French is a Lecturer in Computer Science at the University of Nottingham. His area of research is developing novel image analysis methods, specifically for biological images

Tony is Professor of Computer Science at Nottingham University. He has taught image processing and computer vision since 1990 and now leads a team developing image-based plant phenotyping methods.

Amy Lowe is a PhD student in Computer Science at the University of Nottingham. Her research is on image analysis methods using hyperspectral data.

Michael Pound is a Research Fellow and Lecturer in the School of Computer Science, University of Nottingham, UK. My research interests focus on Deep learning applied to plant phenotyping problems.

Who developed the 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.

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