Objectives and Outcomes
Understanding the principles of medical image analysis. Introduction to algorithms used in digital image
editing, as well as to advanced pattern recognition techniques. Students will be able to recognise which
medical image analysis should be applied. They will be able to decide on the appropriate
algorithm for the selected method, as well as to implement it with the help of one of the existing
frameworks or program libraries.
Lectures
Image signal classification and image specificity in medicine. Image preprocessing. Mathematical
morphology and morphological filters. Image segmentation. Distance transformation and shortest path
planning. Image representation. Extraction of properties. Parameter and distribution estimates, nearest
neighbour methods, linear discriminants. Dimensionality reduction. PCA analysis, Fisher discriminant,
selection of a subset of properties. Clustering, Bayesian classifiers, neural networks, support vector
machines. Fractal and multifractal image analysis in medicine. Face recognition and analysis. Document
processing and classification. Watermarks and steganography.
Research work
Studying and analysing scientific journals and other literature, students broaden the
knowledge acquired in the lectures. They are able to understand the basic algorithms used in image
processing and pattern recognition. They expand knowledge by working on a specific problem in the field
of doctoral dissertation. Students are trained to write scientific papers.