3 ECTS credits
80 h study time

Offer 1 with catalog number 4019776DNR for all students in the 1st semester at a (D) Master - preliminary level.

Semester
1st semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Engineering
Department
Electronics and Informatics
Educational team
Jef Vandemeulebroucke (course titular)
Activities and contact hours
18 contact hours Lecture
18 contact hours Seminar, Exercises or Practicals
Course Content

Goal of the course

The goal of this course is to provide an introduction to the field of biomedical signals and images. An overview is given the principle modalities of biomedical signals and images, along with the basic concepts of the most commonly used signal and imaging equipment. Popular signal and image processing techniques are presented with an emphasis on common applications in the biomedical field.

Starting from basic concepts of one-dimensional and multi-dimensional digital signal processing, and fundamental physiological and physical principles, the major bio-electric signal acquisition and imaging equipment are explained. The performances and limitations of these techniques are also discussed such that image quality (noise, scatter, artifacts, etc.) can be clarified and identified. Next, common operations in biomedical signal and image processing will be presented, such as image enhancement, segmentation, feature extraction registration and visualization of medical images.

Contents

  • Introduction to signals, images and digital signal processing: sampling and quantization, linear and stationary systems, convolution and correlation, filtering, Fourier transform, Nyquist criterion
  • Bio-electrical signals (ECG,EEG): principle, instrumentation, acquisition and analysis
  • Biomedical images (X-Ray, CT, MRI, US, SPECT and PET): principle, instrumentation, acquisition and image formation
  • Biomedical image enhancement, filtering and segmentation
  • Feature extraction and quantification
  • Biomedical image registration and visualization

 

Practical sessions and exercises:

The lectures are supported by 4 practical sessions, covering topics from the lectures:

  • Introduction to signals, images and digital signal processing
  • Biomedical signal processing for ECG and EEG
  • Biomedical image enhancement, filtering and segmentation
  • Feature extraction and quantification
Course material
Digital course material (Required) : Slides presented during lectures, Jef Vandemeulebroucke
Handbook (Recommended) : Biomedical Signal and Image Processing, Kayvan Najarian, Robert Splinter, Second Edition, CRC Press, Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742, 9781439870334, 2012
Additional info

Lectures will cover the theorectical part of the course. Practical sessions will consist of exercises in which the concepts seen during the lectures are applied. Practical sessions will be guided by assistants. Reports on the practical sessions can be finalized afterwards.

Learning Outcomes

General Competences

After completing this course, the student will be able to:

  • Apply the basic principles of digital signal processing, load digital signals and images, perform common operations and visualize them
  • Explain the sources of bio-electrical signals, understand the principle of signal acquisition and carry out typical signal analysis operations
  • List the different types of biomedical image modalities, differentiate the underlying physical principle, and summarize the instrumentation, acquisition and image formation
  • Explain the principle of image enhancement, filtering and segmentation; and apply those to biomedical images
  • Explain the goal of feature extraction and quantification, and compare the outcome for different types of images
  • Explain the process of biomedical image registration and visualization, and select the suitable settings depending on the situation

This course contributes to the following programme outcomes of the Master in Applied Computer Sciences:

MA_A: Knowledge oriented competence

1. The Master in Engineering Sciences has in-depth knowledge and understanding of exact sciences with the specificity of their application to engineering
3. The Master in Engineering Sciences has in-depth knowledge and understanding of the advanced methods and theories to schematize and model complex problems or processes
4. The Master in Engineering Sciences can reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. The Master in Engineering Sciences can correctly report on research or design results in the form of a technical report or in the form of a scientific paper
8. The Master in Engineering Sciences can collaborate in a (multidisciplinary) team

MA_B:  Attitude

12. The Master in Engineering Sciences has a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
15. The Master in Engineering Sciences has the flexibility and adaptability to work in an international and/or intercultural context

MA_C:  Specific competence

17. The Master in Applied Computer Sciences has a thorough understanding of the underlying physical principles and the functioning of electronic and photonic devices, of sensors and actuators and is able to use them to conceive information processing systems and more specifically systems of systems
19, The Master in Applied Computer Sciences has knowledge of and is able to use advanced processing methods and tools for the analysis of (big) data in different  application domains
26. The Master in Applied Computer Sciences can apply his/her acquired knowledge and skills for designing smart city or digital health dedicated systems of systems.
27. The Master in Applied Computer Sciences is aware of and critical about the impact of ICT on society.

Grading

The final grade is composed based on the following categories:
Oral Exam determines 65% of the final mark.
Practical Exam determines 35% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

  • Oral exam on theory with a relative weight of 1 which comprises 65% of the final mark.

    Note: Oral exam with written preparation (closed book). After the questions are given, students will be given time to prepare the answers on paper, before explaining these during oral examination.

Within the Practical Exam category, the following assignments need to be completed:

  • Reports on practical sessions with a relative weight of 1 which comprises 35% of the final mark.

    Note: Reports describing the results on the assignments given during the practical sessions will be evaluated. Evaluation will be based on the correctness of the performed assignments, the answers to the questions and the completeness of the reports

Additional info regarding evaluation

Students must participate to the oral exam and complete the reports on the practical sessions. Students must pass both parts (oral exam and practical sessions) in order to pass the course. An exemption for either part can be obtained for the second session, if a passing grade was obtained for that part in the first session. 

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Applied Sciences and Engineering: Applied Computer Science: Standaard traject (only offered in Dutch)
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics