Subject description - BEAM33ZMO

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BEAM33ZMO Medical Image Processing
Roles:PV Extent of teaching:2P+2C
Department:13133 Language of teaching:EN
Guarantors:  Completion:Z,ZK
Lecturers:  Credits:6
Tutors:  Semester:Z


This subject describes algorithms for digital image processing of 2D and 3D images, with emphasis on biomedical applications. We shall therefore concentrate on the most often used techniques in medical image processing: segmentation, registration, and classification. The methods will be illustrated by a range of examples on medical data. The students will implement some of the algorithms during the practice sessions. Because of the very large overlap between courses A6M33ZMO and A4M33ZMO, the courses will be taught together this year.

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.

Course outlines:

Because of the large overlap between courses A6M33ZMO and A4M33ZMO, the courses will be taught together this year.
1. Introduction, digitalization and quantization, intensity transformations, histogram.
2. Interpolation, geometric transformation, 2D/3D linear and non-linear filtering.
3. Noise suppression, Wiener filtering, wavelet filtering and de-noising.
4. Mathematical morphology. Texture and its description.
5. Segmentation, low-level methods (thresholding, region growing).
6. Graph-based segmentation methods.
7. Active contours. Principle component analysis and statistical shape and appearance models.
8. Levelset based segmentation.
9. Registration based on landmarks, elastic and rigid, robust methods.
10. Similarity criteria, optimization methods for elastic registration.
11. Discrete registration methods. Diffeomorphic registration methods. Optical flow.
12. The reconstruction problem with applications to CT, regularization, non-linear methods.
13. Detection and classification, applications in mamography, CT, MRI and ultrasound.
14. Reserve.

Exercises outline:

Individual works will consist of independent practical work involving the use of algorithms covered by the course for analysis of specific medical data.


[1] Sonka M., Fitzpatrick J. M.: Handbook of Medical Imaging, vol.2. SPIE Press, 2000.
[2] Bankman, I. Handbook of Medical Imaging, Processing and Analysis, vol.1. Academic Press, 2000.


The knowledge of basic signal processing methods including a Fourrier transform, and the knowledge of the basic principles of medical imaging methods.


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
MEBIO3_2018 Image processing PV 3
MEBIO_2018 Common courses PV 3
MEBIO4_2018 Signal processing PV 3

Page updated 3.7.2020 17:51:56, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)