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Higher education teachers: Pernuš Franjo
Prerequisits:
Content (Syllabus outline):
Introduction: 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.
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.
Objectives and competences:
The objective of this course 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.
Intended learning outcomes:
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.
Learning and teaching methods:
Lectures, lab works and individual assignements.