The course objective is to provide students with an overview of the computational and mathematical methods in medical image processing and analysis. Several up-to-date automated methods aimed to enhance and extract useful information from medical images, such as X-ray, CT, MRI, PET, will be presented. A variety of diagnostic and interventional scenarios will be used as examples to motivate the methods. The main topics are medical image analysis in clinical practice, segmentation and quantitative analysis (classification and applicability of methods, thresholding, edge- and region-based techniques, model- and atlas-based methods, supervised and unsupervised methods, cluster-based, principal component analysis, statistical shape and appearance models), computer-aided diagnosis (feature selection and extraction, decision functions, distance measures in cluster analysis, statistical classification, fuzzy classification, neural networks, receiver operating characteristics (ROC), successful applications), and image-guided medical procedures (intrinsic and extrinsic information-based tracking and navigation, procedure planning and visualization, registration of pre- and intra-interventional data, validation of registration methods, applications of image-guided procedures). The students will learn how to extract, model, and analyse information from medical images and apply this information in order to help/enhance diagnosis, treatment and monitoring of diseases through engineering techniques.