Kies de Nederlandse taal
Course module: BMB502817
Image Processing
Course info
Course codeBMB502817
ECTS Credits5
Category / LevelM (Master)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Medicine; Graduate School of Life Sciences; Medical Imaging;
Contact personR.M. Allebrandi
Telephone+31 88 7569652
Contactperson for the course
R.M. Allebrandi
Other courses by this lecturer
Teaching period
MASTER  (20/08/2018 to 17/08/2019)
Teaching period in which the course begins
Time slot-: Not in use
Study mode
Remark10 Sep - 9 Nov 2018. Apply via the Study guide
Course application processadministratie onderwijsinstituut
Enrolling through OSIRISNo
Enrolment open to students taking subsidiary coursesYes
Waiting listNo
Course placement processadministratie onderwijsinstituut
After completing the course the student:
  1. is able to choose the most appropriate technique for medical imaging processing and image analysis.
  2. 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
  3. is able to evaluate image processing and analysis techniques using standardized methodology
  4. is able to implement solutions for new medical imaging problems
  5. knows the benefits and pitfalls of computer-aided diagnosis
Period (from – till): 10 September 2018 - 9 November 2018

Course coordinator: Dr. Kenneth Gilhuijs

Dr. Alexander Leemans, UMC Utrecht/Imaging Division, lecturer
Dr. Kenneth Gilhuijs, UMC Utrecht/Imaging Division, lecturer
Renée Allebrandi, MA (course registration)

Course content
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.
Literature/study material used
Book: Image Processing, Analysis, and Machine vision (Sonka, Hlavac, Boyle), as well as handout materials.

At least 2 weeks before start of the course via the study guide.
Mandatory :
Yes, for MIMG students.

Optional for students in other GSLS Master’s programme:

Prerequisite knowledge:
A BSc in
  • (applied) physics
  • (applied) mathematics
  • computer science
  • biomedical engineering
  • science major of University College Utrecht
  • electrical engineering
  • or similar degree
Entry requirements
Required materials
Instructional formats
Computer practical




Final result
Test weight100
Minimum grade5.5

Kies de Nederlandse taal