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13E054ABS - Biomedical image analysis

Course specification
Course title Biomedical image analysis
Acronym 13E054ABS
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies,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
    URL to the subject page https://automatika.etf.bg.ac.rs/sr/13e054mam
    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. 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.
    Literature
    1. M. M. Janković, Biomedical Image Analysis, University of Belgrade - School of Electrical Engineering, electronic book, 2025
    2. C. Ravishankar, S. Pudipeddi. Image processing and acquisition using Python. Chapman and Hall/CRC, 2020. (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. B. Wolfgang. Applied medical image processing: a basic course. CRC Press, 2016. (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, scientific paper presentation and project.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper 30
    Practical lessons 10 Oral examination
    Projects 60
    Colloquia
    Seminars