Medical Image Analysis (MIA)

Course content

Medical diagnosis, prognosis and quantification of progression is in general based on biomarkers. These may be blood or urine markers, but currently, imaging is taking over as a more indicative biomarker for many purposes.
This course will give an introduction to medical image formation in the different scanning modalities: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of segmentation, registration and end with specific machine learning applications in clinical practise. A key to achieving success in the medical image analysis is formal evaluation of methodologies, thus an introduction to performance characterisation will also be a central topic.

We will use techniques from image analysis and real-world examples from the clinic.

The course aims to provide sufficient background knowledge for doing master theses (specialer) as well as student projects within medical image analysis.

The course is primarily aimed at students from computer science, physics and mathematics with an interest in applications to medical image analysis and related technologies.




MSc Programme in Computer Science
MSc Programme in Physics

Learning outcome

The student will at the end of the course have:


Knowledge of

  • Physics of X-ray formation.
  • Computed tomography.
  • Magnetic Resonance Imaging.
  • Functional MRI.
  • Positron Emission Tomography.
  • Single Photon Emission Tomography.
  • Medical statistics.
  • Segmentation/Pixel classification.
  • Shape modelling and statistics.
  • Rigid & Non-rigid registration + Multi-modal registration.
  • Machine learning with medical data
  • Applications in lung diseases.
  • Applications in neurology.


Skills in

  • Explaining the basics of the underlying physics behind medical image acquisition techniques such as CT, MRI and PET. 
  • Explaining the role of medical image analysis in relation to detection and prognosis of pathologies and clinical investigations.
  • Reading and implementing methods described in the scientific literature in the field of medical imaging.
  • Finding and using existing tools within medical image analysis and assessing the quality of the output produced.
  • Applying the implemented methods to medical images with the purpose of analysing a specific pathology.

Competences in

  • Analysing, creating and using pipelines of methods for the purpose of analysing medical images in a scientific context.
  • Understanding the fundamental challenges in medical image analysis.
  • Understanding the representation of images in a computer.

Lectures, exercises, and assignments.

See Absalon when the course is set up.

The students are expected to have a mature and operational mathematical knowledge. Linear algebra, geometry, basic mathematical analysis, and basic statistics are mandatory disciplines.

In the course, we will be using Python as the programming language, and programming skills in Python are highly recommended.

Academic qualifications equivalent to a BSc degree are recommended.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral examination, 15 minutes with no preparation time
Exam registration requirements

To qualify for the oral examination the student must hand in and have all 4 written assignments approved.

Only certain aids allowed

For the oral examination only print outs of the student's own hand-ins are permitted.

For programming tasks specifically, the use of GitHub Copilot or similar AI-based programming tools is permitted.

For learning about topics, ChatGPT or similar Large Language Models is also permitted.

The finite list of allowed AI-tools will be announced in Absalon.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as the ordinary exam.

If student did not qualify for the regular exam, qualification for the re-exam
can be achieved by submission and approval of equivalent assignments, no later than three weeks before the re-exam date.

Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 32
  • Preparation
  • 78
  • Exercises
  • 16
  • Exam
  • 80
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Melanie Ganz-Benjaminsen   (4-6f697682486c7136737d366c73)

Melanie Ganz & Bulat Ibragimov

Saved on the 07-09-2023

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