Image Processing

Coursecode: bmb502817
Coursecoordinator: dr. Kenneth Gilhuijs
Credits: 5 EC
Lecturers: dr. Kenneth Gilhuijs, dr. Alexander Leemans, dr. Alberto de Luca, Djennifer Madzia-Madzou

Course description

This course covers the full roadmap from basic to more advanced techniques that are commonly used in medical image processing. You will learn how to analyse concrete medical questions that arise from medical images, and that can be solved by mathematical analysis of CT, MRI and X-ray. We will take you from theory to design of computer-aided diagnosis systems and Radiomics systems. Examples of such systems are those that automatically detect tumors in CT and MRI scans, that automatically detect micro-aneurysms in retinal images, or that estimate the prognosis of breast-cancer patients based on imaging features that cannot be picked up by the human eye.

Topics include segmentation (dynamic programming, active contours, level sets), image registration, mathematical morphology, texture analysis, pattern recognition (feature spaces, classifiers, support-vector machines and random forests). During the lectures we will provide small practical assignments using a voting system. A computer practicum will be provided to get hands-on experience with the different techniques. In addition, individual assignments are provided consisting of actual problems that were encountered in medical images.

Learning goals

Upon completion of the course the student

  • is able to choose the most appropriate technique for medical image processing and image analysis
  • knows the underlying theory to understand the strengths and weaknesses of common techniques for image segmentation, image registration, image feature extraction and image feature classification
  • is able to evaluate image processing and analysis techniques using standardized methodology
  • is able to implement solutions for new medical imaging problems
  • knows the benefits and pitfalls of computer-aided diagnosis

Literature/study material

Book: Image Processing, Analysis, and Machine vision (Sonka, Hlavac, Boyle) 3rd or 4th edition, as well as handout materials.


The exam consists of 3 parts:

  • Written exam: 80%
  • Written report: 10%
  • Presentation: 10%

The weights indicated above are applied to calculate the final mark. To pass the course the grade for the written test must be a 5.0 or higher and the final mark must be an unrounded 5.5 or higher.

Date: 12 September 2024 – 30 January 2025


Please check the registration procedure for the enrollment deadlines. First year Medical Imaging students will be automatically registered upon entering the Master’s programme.

Other UU and TU/e partnership students can register for this course via Osiris Student.

Students from outside the UU or TU/E partnership can register for this course by sending an email to Please include your name, student number, Master’s programme and the course code.


For directions to the lecture rooms go to Route.