13M054ABS - Medical image computing

Course specification
Course title Medical image computing
Acronym 13M054ABS
Study programme Electrical Engineering and Computing
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition none
    The goal The objective of the course is to offer theoretical and practical knowledge in the field of processing and analysis of 2D, 3D and 4D biomedical images as well as methods applied to extract quantitative parameters utilized in the clinical practice as an aid to the qualitative interpretation.
    The outcome Students will be able to use software packages to process, analyze and visualize biomedical images, understand and effectively combine existing algorithms to solve specific problems, as well as develop new methods of computer-assisted diagnostics.
    Contents of lectures Basic concepts of image processing and enchancement for various medical imaging modalities (radiography, CT, ultrasound, nuclear medical imaging, MRI, multimodal systems): methods in spatial and frequency domain, Wavelet transformation. Image registration methods. Image segmentation methods. Feature extraction and classification. Machine learning approaches. Methods of 2D and 3D visualization.
    Contents of exercises Mastering software support for algorithms for biomedical image processing, analysis and visualization. Application of algorithms in medical images from open databases as well as studies obtained in cooperation with clinical institutions.
    1. Paul Suetens, "Fundamentals of Medical Imaging", Cambridge University Press, Second Ed., 2009. (Original title)
    2. A.P.Dhawan: Medical Image Analysis. Wiley-IEEE Press, Second Ed., 2011. (Original title)
    3. J.L. Semmlow, B. Griffel, "Biosignal and Medical Image Processing", CRC Press, Taylor&Francis Group, LLC, Third Ed., 2014. (Original title)
    4. M. Sonka, J.M. Fitzpatrick (Eds.), "Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis", SPIE, 2000. (Original title)
    5. V.J. Schmid, A. Meyer-Baese, "Pattern Recognition in Medical Imaging", Academic Press, Second Ed., 2014. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 2
    Methods of teaching Lectures, computer exercises, homework assignments and project.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper
    Practical lessons 10 Oral examination 30
    Colloquia 20
    Seminars 40