Subject description - BAM33ZMO

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BAM33ZMO Medical Image Processing
Roles:PV, PS Extent of teaching:2P+2C
Department:13133 Language of teaching:CS
Guarantors:Kybic J. Completion:Z,ZK
Lecturers:Kybic J. Credits:6
Tutors:Baručić D., Kybic J. Semester:Z

Web page:


This course covers the most used advanced image analysis methods, with emphasis on images from medical and biological modalities, from microscopy, to ultrasound, MRI, or CT, including time sequences.

Study targets:

Learn the principles and usage of basic algorithms for medical image processing, such as registration, segmentation and classification. The students will learn to implement some of the algorithms.


We describe the most used algorithms for solving the key problems in this area - preprocessing, segmentation, registration, reconstruction and classification - and their use in applications. We show how to deal with the specifics of medical data such as non-linear transformation, 3D data, large variability, lack of reliable keypoints, lack of labeled training data etc. This course assumes the knowledge of basic image processing algorithms, as taught for example in the Digital Image course. It complements the Computer Vision Methods course, which covers techniques for images from standard optical cameras.

Course outlines:

1. Segmentation - active contours, level sets
2. Segmentation - shape models,
3. Segmentation - superpixels, random walker, GraphCuts, graph search, normalized cuts
4. Segmentation - texture, texture descriptors, textons
5. Segmentation - CNN, U-net
6. Detection of cells and nuclei
7. Detection of vessels and fibers
8. Detection of nodules and mammographic lesions
9. Localization of organs and structures
10. Registration - ICP, coherent point drift, B-splines, rigid registration, multiresolution
11. Registration - rigid, elastic, daemons
12. Registration by CNN

Exercises outline:

Individual works will consist of independent practical work in a computer laboratory involving the use of algorithms covered by the course for analysis of specific medical data. Some algorithms will be implemented from scratch and some using existing freely available libraries and toolkits. Apart from a general overview, the students will gain a deeper understanding of some of the methods and will learn to apply them to practical problems.


1. Toennies: Guide to Medical Image Analysis, Springer 2012
2. Deserno: Biomedical Image Processing, Springer 2011
3. Yoo: Insight into images. Taylor & Francis, 2004
4. Birkfellner: Applied Medical Image Processing, CRC Press 2011
5. Jan: Medical Image Processing, Reconstruction and Restoration, CRC Press 2006
6. Dhawan: Medical Image Analysis, IEEE Press, 2003
7. Šonka, Fitzpatrick: Handbook of Medical Imaging: Volume 2, Medical Image Processing and Analysis, SPIE Press,


Programming, the knowledge of basic image analysis methods and basic principles of medical imaging devices.


image processing, medical imaging, registration, segmentation, classification, interpolation, detection, reconstruction, noise suppression, active contours.

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPBIO1_2018 Bioinformatics PV 3
MPBIO4_2018 Signal processing PV 3
MPBIO2_2018 Medical Instrumentation PV 3
MPBIO3_2018 Image processing PS 3

Page updated 24.7.2024 09:51:47, semester: Z/2024-5, Z,L/2023-4, L/2024-5, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)