| Course label : | BME315 Signal processing, Part 2 |
|---|---|
| Teaching departement : | MSO / Structures, Mechanisms and Construction |
| Teaching manager : | Mister OLIVIER MAYEUR |
| Education language : | |
| Potential ects : | 2 |
| Results grid : | |
| Code and label (hp) : | MR_BME_S3_MSO_SP2 - BME315 Signal processing, 2 |
Education team
Teachers : Mister OLIVIER MAYEUR
External contributors (business, research, secondary education): various temporary teachers
Summary
This signal processing course series focuses on the application of image processing techniques to biomedical applications. The progression of courses covers key aspects of image processing, mathematical morphology, segmentation, and the integration of eye tracking technology.
Educational goals
Course 1 : Image processing intro Introduction to image processing, covering acquisition, representation, and enhancement. Students engage in hands-on exercises using python. Course 2 : Mathematical morphology Exploration of mathematical morphology as a tool for shape and structure analysis in ultrasound images. Topics include fundamental principles, morphological operations, and applications in medical imaging. Course 3 : Basic mathematical morphology exercises (Lab1) A hands-on laboratory session reinforcing the principles covered in mathematical morphology. Students engage in practical exercises implementing dilation, erosion, and other morphological operations. The lab enhances proficiency in applying these operations and troubleshooting challenges commonly encountered in signal processing. Course 4 : Segmentation, lesson and practices Building on the foundational knowledge, this course focuses on segmentation techniques in signal processing. Students learn about various segmentation algorithms, challenges in segmentation, and practical applications. The course includes hands-on group exercises, allowing students to analyze and implement segmentation methods for real-world scenarios. Course 5 : Eye tracker The final course introduces the integration of eye tracking technology into signal processing. Students gain insights into the principles of eye tracking, eye movement patterns in signal analysis, and practical applications. The course includes hands-on experience with eye tracking devices, emphasizing their role in optimizing signal processing interpretation, especially in dynamic scenarios.
Sustainable development goals
Knowledge control procedures
Continuous Assessment / Final Exam
Comments: None
Online resources
LMS learning management system (Moodle) with all course documents, corrected exercises, publication, forum.
Pedagogy
Lectures, Practices, Homework, Report Examples and application based on illustrations and concrete situations from biomedical engineering and research.
Sequencing / learning methods
| Number of hours - Lectures : | 0 |
|---|---|
| Number of hours - Tutorial : | 0 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 24 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Signal Processing part1, python
Maximum number of registrants
Remarks
None