Courses Taught

Description: Medical imaging systems and physical principles of medical imaging modalities from the cellular to the tissue level. Medical image reconstruction methods, as well as 2D and 3D medical image processing. Image processing techniques: Registration, Data-Fusion, Segmentation and Normalization. Algorithms for the description and retrieval of medical images by content. Picture archiving and communication systems (PACS). Introduction to the analysis of gene-expression data.

Description: Basic introduction to physiology for engineers and computer scientists. Introduction to cellular dynamics and resting potential. Description of action potentials. Basic principles of the cardiovascular system: blood pressure, measurement of blood flow and volume. Digital signal processing and algorithms for biomedical signal analysis. Computer analysis for ECG and EEG: algorithms and software development for diagnosis and research.

Description: Analysis of microarray images with image processing and statistical analysis tools. Introduction to Bioinformatics: databases, tools and open source software. Bioinformatics applications in systems biology, pharmacogenomics and personalized medicine. Basic principles of modelling and methods for modelling physiological systems (PS). Use of Simulink for analysis and simulation of PS. Principles of cardiovascular system and modelling examples with Simulink. Principles of nervous system and modelling examples of neural function with electrical circuits. Introduction to Pharmacokinetics and Pharmacogenomics with application in image analysis of MRI data.

Description: Introduction to Digital Image Processing. Spatial Filtering and Neighboring operators. Image enhancement with point processing operations, brightness transformations and histogram equalization. Fourier analysis, Discrete Fourier Transform and Image enhancement in the frequency domain. Image enhancement and periodic noise removal in the frequency domain with the use of filters. Image restoration and Image sharpening in the spatial and frequency domain. Morphological Image Processing.

Description: Imaging and computer vision are two neighboring research areas gaining great attention from the research community during the last years. This course focuses on the analysis of the patterns in visual images with the view to understanding the objects and processes in the world that generate them. This subject is cross-disciplinary, drawing on mathematics and statistics, physics, optics, physiology, and information theory, as well as computer science, and has many applications including remote sensing, multimedia, surveillance, manufacturing, robotics, medical imaging, human computer interaction. Major topics include optics, image representation, feature extraction, image processing and analysis, object recognition, motion estimation, 3D and multi-view imaging. The emphasis is both on learning mathematical concepts and techniques and on their implementation (Matlab) to solve real vision and imaging problems.