Course detail

Advanced Methods in Image Processing

FEKT-MPA-AB2Acad. year: 2022/2023

The course is designed as an extension of the previous course MPA-ABO (Analysis of Biomedical Images), which is placed in the 3rd semester of the master's study program. The form of teaching is project-based, where students solve assigned tasks from various areas of image data processing within selected teams. Specifically, the following areas are included: image noise suppression, image restoration, landmark detection and feature extraction, stereoscopy, camera calibration methods, disparity map estimation, 3D object reconstruction, advanced methods for image matching, object tracking, and optical-based motion detection flow, image segmentation.


Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Offered to foreign students

The home faculty only

Learning outcomes of the course unit

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. 


The following knowledge is required to enroll in this course:

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). 


Not applicable.

Planned learning activities and teaching methods

The course runs in blocks - 7 weeks / 7 hours + 3 hours. Lectures are conducted in the form of mandatory seminars, which immediately follow on with mandatory computer exercises (solving joint projects in groups in the form of hackathons).  

Assesment methods and criteria linked to learning outcomes

The conditions for successful completion of the course are determined by the annually updated Announcement of the subject guarantee.

- 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) 

Course curriculum

1. Advanced methods for noise suppression (basic methods and advanced approaches - anisotropic diffusion, total variation, deep learning).

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). 

Work placements

Not applicable.


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.


Specification of controlled education, way of implementation and compensation for absences

The course runs in blocks - 7 weeks / 7 hours + 3 hours. Lectures are conducted in the form of mandatory seminars, which immediately follow on with mandatory computer exercises (solving joint projects in groups in the form of hackathons). 

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

JAN, Jiri, 2019. Medical Image Processing, Reconstruction and Analysis: Concepts and Methods. Second Edition. Boca Raton: CRC Press. ISBN 9781138310285. (EN)
Rudin, L. I. et al.: Nonlinear total variation based noise removal algorithms, Physica D vol. 60, 1992, pp. 259-268 (EN)
Kundur, D., Hatzinakos, D.: Blind image deconvolution, IEEE Signal processing magazine, 1996, pp. 43-64 (EN)

Recommended reading

Not applicable.


Classification of course in study plans

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

Type of course unit


Exercise in computer lab

52 hours, compulsory

Teacher / Lecturer