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: The course aims to provide students with theoretical and practical knowledge of basic machine vision concepts related to computational geometry, connected components analysis, shape analysis, image topology and morphology, edge detection and detection of points of interest (Harris method, Hough transform), image registration, image segmentation and object recogntition.

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.

Description: The course is a fundamental for Electrical Engineers, especially in the directions of Electronics, Systems & Computers, and Telecommunications & Information Technology within the department’s curriculum. This course introduces the fundamental concepts of signals and systems in both continuous-time and discrete-time domains. Topics include basic signal operations and properties, elementary signals, and the analysis and description of linear time-invariant systems using convolution, impulse and step responses, and transfer functions. The course covers continuous-time Fourier and Laplace transform techniques for system and frequency response analysis, as well as discrete-time signal representation, sampling, quantization, and reconstruction. Discrete-time systems are studied through difference equations, the Z-transform, and the discrete Fourier transform. Emphasis is placed on practical understanding through computational applications and simulations using MATLAB and/or Python.

Description: Βasic principles of medical image analysis focusing on both classical approaches for image segmentation, registration, quantification, texture analysis and filtering, as well as on AI approaches, including radiomics and deep learning, for diagnostic/prognostic model development.