Medical Image Processing

The course is not on the list Without time-table
Code Completion Credits Range Language
BAM33ZMO Z,ZK 6 2P+2C Czech
The course cannot be taken simultaneously with:
Medical Image Processing (A6M33ZMO)
Medical Image Processing (BEAM33ZMO)
The course is a substitute for:
Medical Image Processing (A6M33ZMO)
Jan Kybic (guarantor)
Jan Kybic (guarantor)
Department of Cybernetics

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.


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

Syllabus of lectures:

1. Introduction. Specifics of medical imaging. Non-linear filtering, PDE, numerical methods.

2. Texture and texture descriptors.

3. Wavelet transform for texture descriptors and noise reduction. Compressed sensing.

4. Active contour segmentation in 2D and 3D. Level sets in 2D and 3D. Fast algorithms.

5. Segmentation in feature space. Shape description and analysis.

6. Segmentation as optimal path searching (random walker), segmentation as discrete optimization.

7. Nonlinear registration with keypoints. Registration as similarity maximization. Similarity criteria.

8. Multimodal registration, hierarchical methods. Regularization. Diffeomorphic methods.

9. Sequence registration. Optical flow. Registration as discrete optimization.

10. Detection and classification as machine learning problem, multistage methods.

11. Tomographic reconstruction for general geometries, iterative and statistical methods. MRI reconstruction methods.

12. Visualization of 3D surface and volume data.

13. Application examples I - cardiology, lungs, mamography, ultrasound, and CT.

14. Application examples II - tumor detection, microscopy and histopathology, MRI.

Syllabus of tutorials:

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.

Study Objective:

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.

Study materials:

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,


Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2020-08-15
For updated information see http://bilakniha.cvut.cz/en/predmet5467606.html