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Introduction to Image Analysis for Plant Phenotyping

Understand the basics of image analysis and learn how to collect, manipulate, and analyse data from images for plant phenotyping.

The sun setting behind a close up of heads of Barley plants in a field

Delve into image analysis and its practical application in plant phenotyping

This six-week course from the University of Nottingham will introduce you to the fundamentals of image analysis and its applications in plant phenotyping.

You’ll learn how to use imaging technologies to collect data from images and perform detailed analyses. By the end of the course, you’ll be able to extract meaningful information from image data without any destruction or harm to the subjects of your study.

Discover how to use Python programming for data analysis in Fiji

You’ll develop your skills using Fiji, also known as ImageJ.

With the help of your educators, you’ll discover how to use Python to perform simple imaging tasks in Fiji. You’ll learn from the ground up, starting with Python basics, such as if statements, for and while loops, and work your way to setting pixel values and dilating regions in code.

Learn how to use image segmentation and noise reduction for image processing

While image processing and analysis is transforming the world of bioscience, there are still significant challenges and bottlenecks to progress. One of these consistent challenges is image quality.

On this course, you’ll learn to combat poor image quality through techniques such as noise reduction and removal, image segmentation, and filtering. You’ll even learn to reconstruct 3D images and motion video in order to find meaningful data.

Study with the experts at the University of Nottingham

The educators at the University of Nottingham are experts in their field, with experience in developing novel image analysis and image-based plant phenotyping methods.

With their professional insight and guidance, you’ll be empowered to continue the evolution of plant phenotyping through image analysis.

Syllabus

  • Week 1

    Introduction to image analysis for plant phenotyping

    • Welcome and Introductions

      Welcome to the course.

    • What is Image analysis?

      What do we mean by image analysis? And how do we apply it to typical plant phenotyping problems?

    • Typical image analysis problems in plant phenotyping

      In plant phenotyping you often want to count or measure some feature of a plants physical shape. An overview of the ways in which image analysis can help you do this, including image segmentation, object and feature detection.

    • Images as data

      We will discuss exactly what digital image data consists of, and what you need to consider to get high quality digital images for use in your analysis.

    • Image Formats and Data Types

      A quick run through of common digital image formats, with advice on what format to use in different contexts

    • Summary and review

      What have we learned so far? And what will we look at next week?

  • Week 2

    Tools for image analysis

    • Introduction to Fiji (ImageJ)

      We focus on one tool in particular, Fiji (also known as ImageJ). We show how do perform simple tasks such as loading images, measuring image features and batch processing. We also introduce ImageJ scripting.

    • Image thresholding

      A common task in image analysis is binary thresholding, or dividing an image into two regions representing some foreground object and a background object. In this activity we look into various ways we can do this.

    • Python basics

      This activity will introduce you to the basics of running Python code on your computer, and show several different ways to do so. We will also show the basics of navigating using a command line, and how to install Python packages.

    • Summary and review

      What have we learned so far? And what will we look at next week?

  • Week 3

    Coding for image analysis

    • Introduction to coding for image analysis

      An introduction to programming using Python, with particular emphasis on its use in image analysis. We introduce functions, variables and data types, and show how Python can be used within Fiji.

    • The building blocks of programming

      An introduction to the basics of Python programming, including For and While loops, and decision making using If statements

    • More complex programs: Dilation and Erosion

      Now we have more programming building blocks in Python we can start piecing them together to perform simple image analysis tasks in Fiji. In particular we look at the morphological operations dilation and erosion.

    • Summary and review

      What have we learned so far? And what will we look at next week?

  • Week 4

    Common tasks in image analysis

    • Noise reduction

      An introduction to the types of noise envountered in image data and how to reduce it. Covers convolution techniques such as Gaussian filtering.

    • Contrast enhancement

      A look at how image clarity can be improved via contrast enhancement, including the use of histogram equalisation.

    • Counting and labelling via Segmentation

      A look at how to identify and quantify image features. Covers image segmentation, including classification, clustering and spatial grouping approaches

    • Feature-based methods

      Often we seek to find point or edge features in our images rather than regions. We look at edge and corner detection using convolutional methods such as the Sobel filter.

    • Model-based approaches

      What do we mean by model-based approaches to image segmentation? We look in detail at the example of Active contours, also known as Snakes

    • Summary and review

      A quiz and summary of the week's material

  • Week 5

    Beyond individual 2D images

    • Video data and motion detection

      A look at common image analysis techniques used when dealing with sequences of images or video, including pixel-based methods, motion detection and background subtraction

    • Measuring motion and object tracking

      We continue our look at motion in video data by first seeing how motion of pixels is measured using optic flow. Next, we look at how objects are tracked using model-based methods such as active contour models

    • Volumetric images

      An introduction to capturing, viewing and analysing volumetric image data

    • Other image data types

      Our look at image data types beyond simple 2D concludes by looking at mosaicing of aerial images, and hyperspectral and multispectral images

    • Summary and review

      A review and summary of 'Beyond 2D images'

  • Week 6

    3D image reconstruction and course summary

    • 3D imaging in plant pheotyping

      What 3D imaging techniques are available and why are they useful in plant phenotyping. We discuss common data types and give examples of their uses.

    • Image based reconstruction

      An overview of the common methods of 3D image reconstruction

    • Case study: 3D reconstruction of plant canopies

      To round off our introduction to 3D image reconstruction we look at two related case studies from the University of Nottingham where 3D reconstruction techniques were used to model plant leaf canopies.

    • Discussion and next steps

      A discussion on what we have covered in the course, and how it feeds into more advanced topics such as machine learning.

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

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Learning on this course

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...

  • Explain what image analysis is, and how it relates to plant phenotyping
  • Explore the kind of data found in a digital images
  • Practice using software tools which can be used in image analysis
  • Code some simple tasks to access data in images
  • Describe some common tasks in image analysis, such as noise removal, labelling of objects and counting of items
  • Investigate some more advanced techniques, such as 3D data and hyperspectral image analysis concepts

Who is the course for?

This course is designed for researchers and professionals working in the field of plant phenotyping.

It will also be beneficial for those working in bioscience disciplines who want to learn more about image analysis and its applications.

What software or tools do you need?

Any software needed for the course is available to download for free and introduced as part of the course content.

Who will you learn with?

Andrew French is a Professor of 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.

Nathan Mellor is a Post-Doc at the University of Nottingham. His research background is mathematical models of plants and plant tissues, using a range of programming and image analysis methods.

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.

University of Lincoln

The University of Lincoln is rated in the top 20 UK universities for student satisfaction in the Guardian University Guide 2022 and the Complete University Guide 2022 and in the top 30 UK universities overall in the WhatUni Student Choice Awards 2020. More than half of our research is judged to be internationally excellent or world leading (Research Excellence Framework, 2014).

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