course: Biomedical Image Processing

teaching methods:
lecture with tutorials
computer based presentation, black board and chalk
responsible person:
Prof. Dr.-Ing. Georg Schmitz
Dr.-Ing. Stefanie Dencks (ETIT), wiss. Mitarbeiter (ETIT)
offered in:
summer term

dates in summer term

  • start: Friday the 05.04.2019
  • lecture Fridays: from 08:15 to 09.45 o'clock in ID 03/419
  • tutorial Tuesdays: from 10:15 to 11.45 o'clock in ID 03/139


Date according to prior agreement with lecturer.

Form of exam:oral
Registration for exam:FlexNow


The students are qualified to apply reliably the basic principles of the two- and multidimensional signal processing. They master techniques and strategies to solve independently typical problems in image processing. Due to the tutorials they have advanced programming skills in Matlab. Because of the application area of medical image processing the students are qualified to analyse multidisciplinary problems.


Basic principles and specific methods of image processing are introduced, which are particularly applied to medical image data. However, several of these methods are also applied in other application areas, e.g. in industrial image processing.

In the first section, both the reception by the human visual system is outlined, and the students are familiarized with the definitions and basic principles of image processing as well (discretization, sampling theorem, global parameters of images). The second section imparts knowledge of the most important operations in the image domain (histogram modulation, filtering, morphological operations, geometric operations, distance transform, ...). The third section comprises methods of information extraction (segmentation algorithms, texture analysis, description of shape). The fourth section focuses on classification and various methods of machine learning (e.g. support vector machines, deep learning). The topic of the fifth section is image restauration. Additionally, an overview of image registration and 3D-visualization is given.



recommended knowledge

Knowledge of system theory, Fourier transform, and signal processing equivalent to the level of Bachelor in Electrical Engineering and Information Technology are a prerequisite. Basic programming skills in Matlab are advantageous.


  1. Lehmann, Thomas, Oberschelp, Walter, Pelikan, Erich "Bildverarbeitung für die Medizin", Springer, 1997
  2. Campisi, Patrizio, Egiazarian, Karen "Blind Image Deconvolution. Theory and Applications", CRC Press, 2007
  3. Fischer, Max, Haberäcker, Peter, Nischwitz, Alfred "Computergrafik und Bildverarbeitung", Vieweg Verlag, 2007
  4. Pratt, William K. Pratt "Digital Image Processing", Wiley & Sons, 1978
  5. Eddins, Steve L., Gonzalez , Rafael C., Woods, Richard E. "Digital Image Processing Using MATLAB", Gatesmark, 2009
  6. Jähne, Bernd "Digitale Bildverarbeitung", Springer, 2010
  7. Wiltgen, Marco "Digitale Bildverarbeitung in der Medizin", Shaker, 1999
  8. Jain, Anil K. "Fundamentals of Digital Image Processing", Prentice Hall, 1989
  9. Asyali, Musa Hakan, Demirkaya, Omer, Sahoo, Prasanna K. "Image Processing with MATLAB. Apllications in Medicine and Biology", CRC Press, 2009
  10. Boyle, Roger, Hlavac, Vaclav, Sonka, Milan "Image Processing, Analysis, and Machine Vision", Brooks Cole, 1999
  11. Oppelt, Arnulf "Imaging Systems for Medical Diagnostics", Publicis Corporate Publishing, 2005
  12. Handels, Heinz "Medizinische Bildverarbeitung", Teubner Verlag, 2000


Registration is carried out via the E-Learning Portal Moodle of the Ruhr-Universität Bochum. The required information is provided in the first lecture.