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Medical images and signals

This is the study programme for 2020/2021.

Medical data, in the form of signals and images, is largely used as an important part of the diagnostics. This subject deals with some key techniques for collecting such data. The theme is seen in relation to signal and image processing as well as machine learning, which are as core subjects in the study program, as such methods can be used for automatic segmentation, interpretation and analysis of signals and images. In modern diagnostics, automatic data analysis can be included as decision support.
The the following techniques will be emphasized: Electrocardiography (ECG), Electroencephalography (EEG), Ultrasound, X-ray, Magnetic Resonance Imaging (MR), Computer Tomography (CT), Angiography.

Learning outcome

Knowledge: The purpose of the course is to provide students with a technological background insight into techniques for the formation of medical diagnostic important signals and images. Such medical data should then be seen in the context of techniques and knowledge from other subjects. Students will learn about a number of different techniques for collecting medical diagnostic data. The following will be emphasized: Electrocardiography (ECG), electroencephalography (EEG), ultrasound, x-ray, magnetic resonance imaging (MR), computer tomography (CT), angiography, etc. Students will learn about the principles, operations and applications of these techniques, for example by means of sample signals and images.
Skills: The students should be able to explain the principles behind some techniques for collecting medical diagnostic signals and images. The student should be able to recognize and understand the meaning of specific characteristics from different types of images and signals.
General competence: After taking this course, students will be able to understand the connection between medical diagnostic signals and images and physiological phenomena.


This course addresses how some selected medical signals and images are formed and the characteristics of these. This is, to some extent, seen in context with the themes and techniques of signal and image processing and machine learning.
The course will focus on principles, modes of operation, applications, and study of example signals and images for some common techniques for collecting medical diagnostic data. The following techniques will be highlighted:
- Electrocardiography (ECG)
- Electroencephalography (EEG)
- Ultrasound
- X-ray
- Magnetic Resonance Imaging (MR)
- Computer tomography (CT)
- Angiography, from X-ray and MRI

Required prerequisite knowledge


Recommended previous knowledge

BIO110 Anatomy and Physiology for Biological Chemistry, ELE500 Signal Processing, ELE510 Image Processing and computer vision, ELE520 Machine learning


Weight Duration Marks Aid
Written exam1/14 hoursA - F

Coursework requirements

Mandatory assignments
1-2 mandatory assignments must be approved to get access to exam.

Course teacher(s)

Course teacher
Mahdieh Khanmohammadi
Course coordinator
Kjersti Engan
Head of Department
Tom Ryen

Method of work

4-6 lectures a week. Mandatory assignments in addition.

Open to

Admission to Single Courses at the Faculty of Science and Technology
Robot Technology and Signal Processing - Master's Degree Programme

Course assessment

Through forms and / or discussions according to current university guidelines.


Literatur will be published as soon as it has been prepared by the course coordinator/teacher

This is the study programme for 2020/2021.

Sist oppdatert: 15.08.2020