A graduate of the course is able to:

- recommend and critically evaluate the suitability of individual methods of medical image analysis for a specific purpose based on the theoretical and practical knowledge acquired in the subject,

- implement specific methods on a suitable software platform, or using commercial software,

- be a valid member of a research/experimental interdisciplinary team in the field of biomedical image data analysis,

- think as a team and practically solve assigned project tasks in a limited time,

- communicate effectively within the research or development team when solving assigned practical tasks. 

1) Processing and analysis of signals (theory of analog and digital signals, filtering, Fourier and wavelet transformation, spectral analysis).

2) Processing and analysis of images and other multidimensional signals (theory of nD signals, image restoration methods, image segmentation methods, texture analysis, image data reconstruction methods).

3) Basic knowledge of machine learning methods and statistical analysis (linear classifiers, clustering methods, neural networks, SVM, PCA, probability theory).

4) Mathematics at the technical college level (derivatives, integrals, solving integrodifferential equations, optimization tasks).

The main prerequisite is the successful completion of the previous course MPA-ABO (Analysis of Biomedical Images). The composition of the MPA-AB2 course is closely related to the material discussed in this course (MPA-ABO). 

- full participation in lectures and exercises

- preparation and delivery of a presentation on a given topic

- up to 100 points in the final exam (the condition for passing the course is to obtain at least 50 points) 

2. Selected image restoration methods (distortion models, blind deconvolution, Tikhonov regularization, deep learning).

3. Detection of points and feature extraction (SIFT, SURF, and others).

4. Stereoscopy, camera calibration methods, disparity map estimation, reconstruction of 3D objects.

5. Advanced methods for image registration (flexible approaches, mark correspondence, ICP method, Elastix program).

6. Object tracking and motion detection methods based on optical flow.

7. Advanced image segmentation methods (graph-based methods and Markov random fields). 

The course aims to familiarize students in the last semester of the master's study program with selected advanced methods in the field of image processing and computer vision, which are applicable to a wide range of applications. The goal is to acquire the appropriate theoretical basis of the discussed methods and, within team projects, to be able to practically apply the acquired knowledge in order to solve the selected task.

• Programme MPC-BTB Master's 2 year of study, summer semester, compulsory-optional