Course detail

Analysis of Biomedical Images

FEKT-MPA-ABOAcad. year: 2023/2024

The subject is oriented towards providing overview of the methods of biomedical image analysis, and a good insight into their concepts, as related to the properties of the medical image data obtained by individual imaging modalities used in medicine and biology.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Offered to foreign students

Of all faculties

Entry knowledge

The generic knowledge on the Bachelor´s degree level is requested, namely in the area of mathematics, programming in Python and signal processing.

Rules for evaluation and completion of the course

- obtaining at least 15 out of 30 credit points and full active presence in demonstration exercises
- successful passing of final written exam (up to 70 points, min. 35 points)

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
- obligatory computer-lab tutorials - programming in Python
- voluntary lectures


The goal of the course is to enable the students gaining an overview of, and insight into, the methods of medical image analysis; acquiring practical experience in software realisation of the methods.
The graduate of the course is capable of:
- recommending and critically evaluating suitability of individual methods of medical image analysis to a particular purpose, based on theoretical and practical knowledge gained in the course,
- implementing these methods on a suitable software platform, possibly with commercial software,
- being a valid member of a research / experimental interdisciplinary team in the area of biomedical image data.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

J. Jan: Medical Image Processing,Reconstruction and Restoration, CRC Taylor and Francis 2006 (EN)
M. Nixon: Feature Extraction and Image Processing for Computer Vision, Academic Press (EN)
Gengsheng Lawrence Zeng: Image Reconstruction, de Gruyter, 2017 (EN)
J. Jan: Medical ImageProcessing, Reconstruction and Analysis 2nd edition CRC Press, Taylor & Francis 2020 (EN)

Recommended reading

M. Sonka, V. Hlavac: Image Processing, Analysis And Machine Vision, Cengage Learning, 2017 (EN)


Classification of course in study plans

  • Programme MPC-BIO Master's, 1. year of study, summer semester, compulsory
  • Programme MPAD-BIO Master's, 1. year of study, summer semester, compulsory
  • Programme MPC-BTB Master's, 1. year of study, summer semester, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Discrete Image and Its Representation - Mathematical Formulations of Image Discretization and Matrix Representation
2. Discrete 2D Transformations - Mathematical Formulations of Basic Transformations for Image Processing
3. Discrete 2D Operators - Mathematical Foundations of Main Operators in Image Processing (Local, Point-wise, Global)
4. Image Enhancement - Noise Suppression, Contrast and Color Adjustment, Sharpening, Edge Highlighting
5. Feature Extraction from Image Data - Problem Formulation, Basic Overview, and Principles of Methods
6. Edge Representation - Detection and Adjustment of Edges in an Image Using Gradient Operators
7. Image Segmentation - Overview and Basic Principles of Common Image Segmentation Methods
8. Morphological Operators for 2D Binary Images
9. Approaches for Image Classification - Overview of Basic Principles
10. Image Registration - Geometric Transformations, Image Interpolation, Similarity Criteria, and Registration Approach with Optimization Algorithm
11. Reconstruction from Tomographic Projections - Mathematical Principles of Basic CT Data Reconstruction Methods from Projections (FBP, Algebraic, and Combined Approaches) 

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer


1. Basic operations with discrete images, image spectrum, and basic 2D functions, verification of Fourier transform properties.
2. Discrete 2D operators - point-wise operations (contrast adjustment, gamma correction, etc.) and local operations (edge enhancement, image sharpening, noise reduction).
3. Feature extraction - demonstration of feature extraction from the frequency domain, convolution-based features, texture analysis, and adaptive filtering.
4. Edge detection - implementation of methods based on 1st derivative, 2nd derivative, and combined approaches.
5 .Image segmentation - simple, adaptive, multi and semi-thresholding, Otsu's method, region growing, watershed, parametric and geometric flexible contours, morphological operations.
6. Object recognition - Implementation of the Hough transform for lines and circles.
7. Implementation of 2D interpolation, introduction and implementation of rigid and flexible geometric transformations, final implementation of image registration and fusion.
8. Reconstruction of tomographic images - Implementation of the Radon transform, sinogram construction, plain and filtered back-projection, reconstruction with fan projections.


13 hours, compulsory

Teacher / Lecturer