Medical Images and Signals (ELE670)

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.

Course description for study year 2023-2024. Please note that changes may occur.


Course code




Credits (ECTS)


Semester tution start


Number of semesters


Exam semester


Language of instruction



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)

- MRI 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 (MRI), 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.

Required prerequisite knowledge


Recommended prerequisites

BIO110 Anatomy and Physiology, ELE500 Signal Processing, ELE510 Image Processing and Computer Vision, ELE520 Machine Learning


Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades

Coursework requirements

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

Course teacher(s)

Course coordinator:

Mahdieh Khanmohammadi

Head of Department:

Tom Ryen

Method of work

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

Open for

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

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto