The course describes the necessary trajectory for the quantitative analysis, the segmentation and the visualisation of medical imaging data. The following steps can be distinguished:
- Image formation.
- Image acquisition.
- Reconstruction from projections.
- Quantitative image analysis.
- Image segmentation.
- Imaging registration.
- 3D visualisation.
The first three steps provide the basic knowledge about medical imaging systems. These have been treated in the course “Imaging techniques” and will not be repeated here. In the current course, imaging is approached at a system level using a linear description of image formation as a central starting point.
The focus of the course is on steps 4 – 7. The end result applies to diagnostics and interventions. The theory of the course is presented in lectures; Matlab image processing labs provide exercise and experience building in the application of the theoretical concepts to medical image data.
Workshops enable the student to master the abstract mathematical descriptions of the image processing algorithms.
The course ends with a project concerning image data from the medical (TM) practice. There is one study stimulating test about three weeks before the end of the course, just before the project starts.
The test counts for 33%, Lab reports for 33%, and project report and presentation for 33%. In addition, each part should be graded by a 4.0 or more.
This course is only open for TM students. BME students who are interested in image analysis and segmentation are advised to attend "Image Processing and Computer Vision" (191210910) as an optional course.