NDAK10005U Medical Image Analysis (MIA)

Volume 2018/2019

MSc Programme in Computer Science
MSc Programme in Physics


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 detection, registration, and segmentation, and end with specific applications in clinical practise. A key to achieve success in the medical image analysis is formal evaluation of methodologies, thus a 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.



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.
  • Rigid & Non-rigid registration + Multi-modal registration.
  • Shape statistics.
  • Applications in Lung diseases.
  • Application in cardiovascular diseases.
  • Applications in joint diseases.
  • Applications in neurology.


Skills in

  • Explaining the basics of the underlying physics behind medical image acquisition techniques such 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 litterature 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.

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. Programming skills are not required, but recommended.
Lectures, exercises, and assignments.
  • Category
  • Hours
  • Class Instruction
  • 28
  • Course Preparation
  • 124
  • Exercises
  • 54
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Continuous assessment (4-6 written homework assignments).
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Oral exam (25 minutes without preparation).

Criteria for exam assesment

See Learning Outcome.