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
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment based on 4-6 written assignments.

The final grade is based on an overall assessment of the assignments.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
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
No limit
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   (4-76707d894f73783d7a843d737a)

Melanie Ganz & Bulat Ibragimov

Saved on the 05-05-2022

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students