Medical Image Analysis

Higher education teachers: Pernuš Franjo

Subject description

Prerequisits:

Recommended prerequisites are basic knowledge of matrix algebra, differential equations and MATLAB.

Content (Syllabus outline):

Introduction: history, importance and areas of computer-aided analysis of medical images.
Medical image sources: X-ray imaging, computed tomography, magnetic resonance imaging, ultrasound, nuclear medicine and molecular imaging.
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.
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.
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.

Objectives and competences:

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.

Intended learning outcomes:

Knowledge and understanding: The students will gain an understanding of the importance of medical images and of the basic principles of image segmentation, registration and information integration. They will gain knowledge to analytically, numerically and experimentally analyse the medical images.

Application: Equip the students with the knowledge and skills required for a career in an image-related field in clinical practice, clinical research, scientific research or technical development.

Transferable skills: The students will be equipped with generic transferrable skills required in a multidisciplinary scientific or clinical research environment. They will be able to use their skills in automated visual inspection in industry.

Learning and teaching methods:

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.

Study materials

1. M. Sonka, J.M. Fitzpatrick (Editors). Handbook of Medical Imaging, Volume 2, Medical Image Processing and Analysis, SPIE Publications, 2009.
2. I.N. Bankman (Editor). Handbook of Medical Image Processing and Analysis, Academic Press, 2nd edition, 2008.
3. T. Peters, K. Cleary (Editors). Image-Guided Interventions: Technology and Applications, Springer, 2008.
4. W. Birkfellner. Applied Medical Image Processing. A basic course Taylor & Francis, 2nd edition, 2014.