CM201V Medial Image Registration 3.0 credits
This course aims to study the most used methods in 3D image registration (alignment of images) and their use in medical applications. Image registration is one of the most fundamental tasks in medical image processing and it is ubiquitous in environments where images are acquired from patients.
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Application
For course offering
Autumn 2024 Start 26 Aug 2024 single courses students
Application code
10081
Content and learning outcomes
Course contents
- Overview of image registration
- Linear registration
- Non-linear registration
- Registration with a priori information
The course consists of lectures, laboratories, mathematical exercises, and an exam. Participants combine basic and advanced software libraries for image registration in Python, including scipy, numpy, SimpleITK, scikit-image, etc. The course also includes introductory labs for students with programming experience but no Python experience.
Intended learning outcomes
Image registration is used to align images from the same or different modalities to each other. Image registration is important for the diagnosis and treatment of various diseases. The course covers concepts, theories, and the most used methods in image registration. The course is focused on solving medically relevant problems.
After completing the course, the participant should be able to:
- Understand the key issues and challenges in image registration
- Describe the main principles and methods and the main differences between them
- Summarize the advantages and disadvantages and scope of different methods
- Identify and understand the mathematical theory behind the most used methods
- Develop and systematically evaluate different methods for solving simplified problems
- Analyze the effect of different parameters of methods in particular situations
- Explain the proposed strategy for solving specific problems
in order to:
- understand the complete workflow for using computational tools for image registration in a medical context
- be able to implement computational solutions in image registration to medically relevant problems
- have a broad knowledge base that can facilitate understanding literature in the field
Literature and preparations
Specific prerequisites
Bachelor's degree in Medical Technology, Engineering Physics, Electrical Engineering, Computer Science or equivalent. At least 6 credits in programming. English B/English 6.
Recommended prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- LAB1 - Laborations and exercises, 1.5 credits, grading scale: P, F
- TEN1 - Written exam, 1.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Offered by
Main field of study
Education cycle
Add-on studies
Contact
Supplementary information
The target group for this course are persons who are not program students at KTH.