Medical Image Analysis

1. Introduction: history, importance and areas of computer-aided analysis of medical images.
2. Medical image sources: X-ray imaging, computed tomography, magnetic resonance imaging, ultrasound, nuclear medicine and molecular imaging.
3. Image segmentation and quantitative analysis: classification and applicability of methods, (adaptive) thresholding, edge-based segmentation techniques, region growing, segmentation with clustering, deformable models,atlas- based methods. Validation of image segmentation methods.
4. Image registration: clinical applications of image registration, classification of registration methods, spatial transformation models, within- and across-modality registration, landmark- based registrations, surface based registrations, intensity based registrations, similarity measures. Validation of registration methods.
5. Image guided procedures: tracking devices, visualization in image-guided procedures, planning, registration of preoperative images, models and plan with intraoperative images, 3D-2D registration, validation of image guided procedures, clinical applications.

To gain understanding of the importance and the basic principles of medical image analysis, which are nowadays an indispensable tool for diagnosis, planning, simulation and execution of medical procedures and for monitoring the effects of therapy and progression of disease. To acquire basic knowledge for analytical, numerical and experimental analysis of medical images.

Lectures throughout the semester if a sufficient number of students select this course. Otherwise, some introductory lectures, followed by individual research, tutorials and seminars under the supervision of the lecturer.