Biomedical Image Analysis

Course Description

The course provide students with an opportunity to gain knowledge in theory and applications of digital image analysis in biomedicine. An overview of biomedical imaging modalities. Image analysis applications for computer-aided diagnosis and image-based interventions. X-ray, computed tomography, magnetic resonance imaging, PET, SPECT, nuclear medicine, ultrasound, OCT. Image enhancement methods. Image registration methods. Surface and volume visualization in biomedicine.

Learning Outcomes

  1. define and describe concepts of biomedical image analysis
  2. list examples of biomedical image analysis applications
  3. explain methods for biomedical image analysis
  4. analyze a practical biomedical image analysis problem
  5. combine acquired knowledge and propose a solution to the given problem
  6. evaluate a practical solution to a biomedical image analysis problem

Forms of Teaching


The lectures present theoretical concepts and algorithms followed by concrete examples.


Team project in which students solve a real practical problem of biomedical image analysis

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 20 % 20 % 20 % 20 %
Mid Term Exam: Written 20 % 40 % 0 %
Final Exam: Written 20 % 40 %
Exam: Written 50 % 80 %

The threshold on the sum of the midterm and the final exam is 50%.

Week by Week Schedule

  1. Overview of imaging modalities, Structural and functional imaging, Basic medical image processing and analysis methods, Basic medical image registration methods, Performance measures, Computer-aided diagnosis, Image-guided interventions
  2. X-ray imaging, computed tomography
  3. Magnetic resonance imaging, nuclear medicine (SPECT, PET)
  4. Ultrasound, optical coherent tomography (OCT) Contrast adjustment, Denoising, Deblurring
  5. Image enhancement
  6. Image restoration, degradation models, inverse and pseudoinverse filter, Wiener filter.
  7. Backprojection, Algebraic reconstruction, Projection geometry, Radon transform, The Fourier slice theorem
  8. Midterm exam
  9. Image segmentation. Boundary detection, Thresholding, Region growing, Watershed segmentation, Segmentation by motion, Mean shift cllustering, Graph-based methods, Hough transform
  10. Deformable models, Statistical atlases, Texture features, Texture analysis
  11. Image registration, salient point detection, point matching, rigid body registration, principal axis registration
  12. Image registration, image similarity measures, landmark-based registration, elastic registration
  13. Visualization for diagnosis and therapy. Surface visualization. Volume visualisation. Virtual reality. User interaction. Intraoperative navigation. Augmented reality.
  14. Project
  15. Final exam

Study Programmes

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Paul Suetens (2017.), Fundamentals of Medical Imaging, 3rd Ed., Cambridge University Press
Rafael C. Gonzalez, Richard Eugene Woods (2017.), Digital Image Processing, 4th Ed., Pearson

For students


ID 222458
  Winter semester
L3 English Level
L1 e-Learning
30 Lectures

Grading System

87 Excellent
75 Very Good
63 Good
51 Acceptable