Duration
6 weeksWeekly study
3 hours100% online
How it works
Introduction to Image Analysis for Plant Phenotyping
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.
Available now
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 a research-focused campus university, described as “the nearest thing Britain has to a truly global university” by The Times. As a member of both the Russell Group and Universitas 21 international network, our reach extends across our campuses in the UK, China and Malaysia, with a diverse student body drawn from over 150 countries.
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Ways to learn
Choose the best way to learn for you!
Subscribe & save
$244.99 for a whole year
Automatically renews
Develop skills to further your career
- Access to this course
- Access to 1,000+ courses
- Learn at your own pace
- Discuss your learning in comments
- Digital certificate when you're eligible
Cancel for free anytime
Buy this course
$79/one-off payment
Fulfill your current learning need
- Access to this course
- Learn at your own pace
- Discuss your learning in comments
- Printed and digital certificate when you’re eligible
Limited access
Free
Sample the course materials
- Access expires 20 Mar 2025
Find out more about certificates, Unlimited or buying a course (Upgrades) Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply. |
Find out more about certificates, Unlimited or buying a course (Upgrades)
Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply.
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