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Arne Seitz,
Romain Guiet,
Olivier Burri,
and
Nicolas Chiaruttini
Nowadays, image-based methods are indispensable for life scientists. Light microscopy especially, has evolved from sketched out observations by eye, to high throughput multi-plane, multi-channel, multi-position and multimode acquisitions that easily produce thousands of information-rich images that must be quantified somehow to answer biological questions.
Read more
Nowadays, image-based methods are indispensable for life scientists. Light microscopy especially, has evolved from sketched out observations by eye, to high throughput multi-plane, multi-channel, multi-position and multimode acquisitions that easily produce thousands of information-rich images that must be quantified somehow to answer biological questions.
Read more
Nowadays, image-based methods are indispensable for life scientists. Light microscopy especially, has evolved from sketched out observations by eye, to high throughput multi-plane, multi-channel, multi-position and multimode acquisitions that easily produce thousands of information-rich images that must be quantified somehow to answer biological questions.
This course will teach you core concepts from image acquisition to image filtering and segmentation, to help you tackle simple image analysis workflows on your own. All examples use open source solutions, in order to allow you to be independent from commercial solutions. Emphasis is made on good practices and typical pitfalls in image analysis. At the end of this course, you will be able to adapt and reuse workflows to suit your specific needs and be equipped with the tools and knowledge to adapt and seek advice from the ever-growing image analyst community of which you will be a part now
The course is taught by senior image analysts with longtime work experience in a service-oriented core facility.
Register for this course and see more details by visiting:
OpenCourser.com/course/awsnxo/image
What's inside
Learning objectives
- Recall digital image formation principles
- Understand human perception and color
- Distinguish between bit-depths
- Use lookup tables
- Perform mathematical operations on images
- Apply filtering to digital images
- Understand and use image segmentation techniques
- Create regions of interest and extract results from segmented images
- How to perform projections and reslicing on images for analysis
- Applying color deconvolution to brightfield images
- Understand the concepts of the imagej macro language
- Recall digital image formation principles
- Understand human perception and color
- Distinguish between bit-depths
- Use lookup tables
- Perform mathematical operations on images
- Apply filtering to digital images
- Understand and use image segmentation techniques
- Create regions of interest and extract results from segmented images
- How to perform projections and reslicing on images for analysis
- Applying color deconvolution to brightfield images
- Understand the concepts of the imagej macro language
Syllabus
Week 1: Digital ImagesIntroduction to digital image formation and how optical systems go from objects to images.
Week 2: ColorsReview of human visual perception and the RGB color model. Introduction to the concepts of image bit-depth and lookup tables.
Read more
Syllabus
Week 1: Digital ImagesIntroduction to digital image formation and how optical systems go from objects to images.
Week 2: ColorsReview of human visual perception and the RGB color model. Introduction to the concepts of image bit-depth and lookup tables.
Read more
Week3: Operating on ImagesIntroduction to image scaling, interpolation, and mathematical operations of images, and why certain bit-depths are more suitable than others.
Week4: FilteringUsing image filtering to enhance or suppress features in an image for easing subsequent analysis. We cover linear, nonlinear and Fourier filtering with emphasis on examples.
Week 5: Image SegmentationIntroduction to image segmentation and overview of available methods (thresholding, clustering, machine learning) and morphological operations.
Week 6: Regions of InterestGoing from analyzed objects to regions of interest and results tables. Emphasis is made on how to best obtain unbiased measurements and produce a reusable image analysis workflows
Week 7: Colors, and dimensionality reductionIntroduction to color models, and color deconvolution. Overview of the concept of dimensionality reduction through image projections and reslicing and application to measuring moving objects.
Extra Week: ImageJ Macro Programming PrimePresentation of basic programming principles applied to the ImageJ Macro Language. Crash course on variables arrays, loops, conditionals, available macro functions and writing custom functions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches core concepts of image analysis workflows used in life sciences, such as acquisition, filtering, and segmentation
Taught by senior image analysts with extensive experience in a service-oriented core facility, indicating expertise in the field
Relevant to life scientists seeking to analyze image-based data, a growing field in biology
Emphasizes good practices and typical pitfalls in image analysis, ensuring learners develop sound methodologies
Provides a crash course on the ImageJ Macro Language, a useful tool for automating image analysis tasks
Uses open-source solutions, allowing learners to apply their knowledge independently and cost-effectively
Save this course
Save Image Processing and Analysis for Life Scientists to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Image Processing and Analysis for Life Scientists with these
activities:
Review 'Digital Image Processing' by Rafael C. Gonzalez and Richard E. Woods
Show steps
Gain a deeper understanding of digital image processing by reading a classic text on the subject.
View
Digital Image Processing, Global Edition
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes on the key concepts.
-
Complete the exercises at the end of each chapter.
Learn about the ImageJ Macro Language
Show steps
Expand your knowledge of ImageJ by learning the basics of the ImageJ Macro Language.
Show steps
-
Find a tutorial on the ImageJ Macro Language.
-
Follow the tutorial to learn the basics of the language.
-
Write a simple macro to perform a task in ImageJ.
Discuss image segmentation techniques with other students
Show steps
Enhance your understanding of image segmentation by discussing it with peers.
Browse courses on
Image Segmentation
Show steps
-
Find a study group or online forum where you can connect with other students.
-
Discuss different image segmentation techniques.
-
Share tips and tricks for using image segmentation software.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice applying color deconvolution to brightfield images
Show steps
Improve your understanding of color deconvolution by practicing applying it to brightfield images.
Show steps
-
Gather a set of brightfield images.
-
Apply color deconvolution to the images using ImageJ or another software package.
-
Compare the results of color deconvolution to the original images.
Attend a workshop on advanced image analysis techniques
Show steps
Gain exposure to new image analysis techniques by attending a workshop.
Browse courses on
Image Analysis
Show steps
-
Find a workshop that is relevant to your interests.
-
Register for the workshop.
-
Attend the workshop and participate in the activities.
Create a tutorial on how to use ImageJ to analyze images
Show steps
Reinforce your knowledge of ImageJ by creating a tutorial for others.
Browse courses on
ImageJ
Show steps
-
Choose a topic that you are familiar with.
-
Write a step-by-step guide on how to use ImageJ to perform the task.
-
Create screenshots or videos to illustrate the steps.
-
Publish your tutorial online.
Contribute to an open-source image analysis project
Show steps
Contribute to the image analysis community by working on an open-source project.
Browse courses on
Image Analysis
Show steps
-
Find an open-source image analysis project that you are interested in.
-
Identify an area where you can contribute.
-
Make a pull request to the project.
Develop a software tool to automate image analysis tasks
Show steps
Apply your knowledge of image analysis to create a useful tool.
Browse courses on
Image Analysis
Show steps
-
Identify a need for an automated image analysis tool.
-
Design and develop the software tool.
-
Test and refine the software tool.
-
Deploy the software tool to users.
Review 'Digital Image Processing' by Rafael C. Gonzalez and Richard E. Woods
Show steps
Gain a deeper understanding of digital image processing by reading a classic text on the subject.
View
Digital Image Processing, Global Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes on the key concepts.
- Complete the exercises at the end of each chapter.
Learn about the ImageJ Macro Language
Show steps
Expand your knowledge of ImageJ by learning the basics of the ImageJ Macro Language.
Show steps
Show steps
Show steps
- Find a tutorial on the ImageJ Macro Language.
- Follow the tutorial to learn the basics of the language.
- Write a simple macro to perform a task in ImageJ.
Discuss image segmentation techniques with other students
Show steps
Enhance your understanding of image segmentation by discussing it with peers.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Find a study group or online forum where you can connect with other students.
- Discuss different image segmentation techniques.
- Share tips and tricks for using image segmentation software.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice applying color deconvolution to brightfield images
Show steps
Improve your understanding of color deconvolution by practicing applying it to brightfield images.
Show steps
Show steps
Show steps
- Gather a set of brightfield images.
- Apply color deconvolution to the images using ImageJ or another software package.
- Compare the results of color deconvolution to the original images.
Attend a workshop on advanced image analysis techniques
Show steps
Gain exposure to new image analysis techniques by attending a workshop.
Browse courses on
Image Analysis
Show steps
Show steps
Show steps
- Find a workshop that is relevant to your interests.
- Register for the workshop.
- Attend the workshop and participate in the activities.
Create a tutorial on how to use ImageJ to analyze images
Show steps
Reinforce your knowledge of ImageJ by creating a tutorial for others.
Browse courses on
ImageJ
Show steps
Show steps
Show steps
- Choose a topic that you are familiar with.
- Write a step-by-step guide on how to use ImageJ to perform the task.
- Create screenshots or videos to illustrate the steps.
- Publish your tutorial online.
Contribute to an open-source image analysis project
Show steps
Contribute to the image analysis community by working on an open-source project.
Browse courses on
Image Analysis
Show steps
Show steps
Show steps
- Find an open-source image analysis project that you are interested in.
- Identify an area where you can contribute.
- Make a pull request to the project.
Develop a software tool to automate image analysis tasks
Show steps
Apply your knowledge of image analysis to create a useful tool.
Browse courses on
Image Analysis
Show steps
Show steps
Show steps
- Identify a need for an automated image analysis tool.
- Design and develop the software tool.
- Test and refine the software tool.
- Deploy the software tool to users.
Career center
Learners who complete Image Processing and Analysis for Life Scientists will develop knowledge and skills
that may be useful to these careers:
Image Analyst
Image Analyst
An Image Analyst processes and analyzes digital images to extract information and insights. This course provides a comprehensive overview of the principles and techniques of digital image processing and analysis, including image formation, color models, filtering, and segmentation. These concepts are essential for successful careers in image analysis, particularly in fields such as medical imaging, remote sensing, and industrial inspection.
Quantitative Imaging Analyst
Quantitative Imaging Analyst
A Quantitative Imaging Analyst develops and applies image processing and analysis techniques to quantify biological data. This course provides a comprehensive overview of the principles and techniques of digital image processing and analysis, including image formation, color models, filtering, and segmentation. These concepts are essential for successful careers in quantitative imaging analysis, particularly in fields such as medical imaging, biology, and materials science.
Computer Vision Scientist
Computer Vision Scientist
A Computer Vision Scientist develops algorithms and techniques to enable computers to interpret and understand digital images. This course provides a solid foundation in the principles of digital image processing and analysis, including image formation, color models, filtering, and segmentation. These concepts are essential for developing computer vision systems for tasks such as object recognition, scene understanding, and medical imaging.
Microscopy Technician
Microscopy Technician
A Microscopy Technician operates and maintains microscopes and other imaging equipment to produce high-quality images for research and diagnostic purposes. This course may be useful in understanding the principles of digital image processing, including image acquisition, filtering, and segmentation. These techniques are essential for optimizing image quality and extracting meaningful information from microscopy images.
Medical Physicist
Medical Physicist
A Medical Physicist applies the principles of physics to the diagnosis and treatment of disease. This course may be useful in understanding the principles of digital image processing, including image acquisition, filtering, and segmentation. These techniques are widely used in medical physics applications such as medical imaging, radiation therapy, and nuclear medicine.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful in understanding the fundamentals of digital image processing, including image acquisition, filtering, and segmentation. These techniques are increasingly important in software development for fields such as medical imaging, computer vision, and robotics.
Systems Biologist
Systems Biologist
A Systems Biologist studies the interactions between the components of a biological system to understand its behavior. This course may be useful in understanding the principles of digital image processing, including image acquisition, filtering, and segmentation. These techniques are increasingly important in systems biology for applications such as cell imaging, gene expression analysis, and drug discovery.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve real-world problems. This course may be useful in understanding the fundamentals of digital image processing and analysis, which are essential for building machine learning models that can process and interpret images.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to draw conclusions and make predictions. This course may be useful in understanding the principles of digital image processing, including image acquisition, filtering, and segmentation. These techniques are becoming increasingly important in statistics for applications such as medical imaging, remote sensing, and quality control.
Data Scientist
Data Scientist
A Data Scientist analyzes and interprets data to extract insights and make predictions. This course may be useful in understanding the fundamentals of digital image processing, including image acquisition, filtering, and segmentation. These techniques are becoming increasingly important in data science applications such as image recognition, medical imaging, and remote sensing.
Research Scientist
Research Scientist
A Research Scientist conducts scientific research to advance knowledge and develop new technologies. This course may be useful in understanding the principles of digital image processing and analysis, which are essential for many research applications, including medical imaging, remote sensing, and materials science.
Biomedical Engineer
Biomedical Engineer
A Biomedical Engineer applies engineering principles to solve problems in biology and medicine. This course may be useful in understanding the fundamentals of digital image processing, including image acquisition, filtering, and segmentation. These techniques are widely used in biomedical engineering applications such as medical imaging, diagnostics, and therapy.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation, such as user manuals, training materials, and white papers. This course may be useful in creating technical documentation for digital image processing and analysis software or equipment. It can help writers understand the principles and techniques involved, enabling them to write clear and accurate documentation.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. This course may be useful in developing web applications that incorporate digital image processing and analysis functionality. It can help developers understand the principles and techniques involved, enabling them to build robust and effective web applications.
Bioinformatician
Bioinformatician
A Bioinformatician designs and builds computational tools and databases to analyze and interpret biological data. This course may be useful in understanding the principles of digital image formation, color models, and image segmentation techniques. These concepts are essential for developing algorithms to process and analyze biological images.
For more career information including salaries, visit:
OpenCourser.com/course/awsnxo/image
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Image Processing and Analysis for Life Scientists.
Classic textbook on digital image processing. It provides a comprehensive overview of the field, covering topics such as image formation, enhancement, segmentation, and compression. It valuable resource for students and practitioners alike.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object recognition, and motion analysis. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of computer vision. It covers topics such as image formation, feature extraction, object recognition, and motion analysis. It valuable resource for students and practitioners alike.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners alike.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners alike.
Provides a clear and concise introduction to the fundamental concepts of image analysis. It covers topics such as image representation, feature extraction, and classification. It good choice for students who are new to the field.
Provides a comprehensive overview of image processing and analysis for the biological sciences. It covers topics such as image acquisition, preprocessing, feature extraction, and classification. It valuable resource for students and practitioners who want to learn how to use image analysis to study biological systems.
Provides a practical introduction to machine learning for computer vision. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for students and practitioners who want to learn how to use machine learning to solve computer vision problems.
Provides a practical introduction to image analysis. It covers topics such as image acquisition, preprocessing, feature extraction, and classification. It valuable resource for students and practitioners who want to learn how to use image analysis to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/awsnxo/image
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Effort
2 to 3 hours per week for 7 weeks
Level
Intermediate
Via
edX
Institution
École Polytechnique Fédérale de Lausanne
Instructors
Arne Seitz
Romain Guiet
Olivier Burri
Nicolas Chiaruttini
Language
English
Transcript
English
Good to know
Teaches core concepts of image analysis workflows used in life sciences, such as acquisition, filtering, and segmentation
Taught by senior image analysts with extensive experience in a service-oriented core facility, indicating expertise in the field
Relevant to life scientists seeking to analyze image-based data, a growing field in biology
Emphasizes good practices and typical pitfalls in image analysis, ensuring learners develop sound methodologies
Provides a crash course on the ImageJ Macro Language, a useful tool for automating image analysis tasks
Uses open-source solutions, allowing learners to apply their knowledge independently and cost-effectively
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