Title of Course: Image Processing
Exam number: XM 2720 (required course), XM 3060 (elective)
Length of course: 2 hours per week
Type of course: lecture
Language: English
Lecturer: Prof. Dr. Th. Buzug
Learning outcomes:
• Development of the basics of the 2D signal processing (colors spaces, image scanning, discretisation of twodimensional signals, discrete geometry)
• Getting to know simple signal processing methods concerning feature extraction, filtering, and contrast adaption
• Getting to know different methods to restore an image
• Getting to know the basics of different image processing methods such as image segmentation and image registration
• Getting to know the mathematical description, numerical solutions, and algorithmic implementation in digital signal processing
• Knowledge about lecturer´s current research projects

Course Requirements: Mathematical knowledge and knowledge in the field of signal-theory

Content:
Medical image processing is an active area of research. This lecture aims at developing a principle understanding for methods used in medical image processing and image analysis. The lecture will cover the mathematical description, the numerical solution as well as the algorithmic implementation of different methods used in medical image processing. Apart from the introduction into very general image processing methods this lecture will also provide some insight into state-of-the-art image processing algorithms. This lecture is not thought as a brief overview of medical image processing and its applications. It is rather thought as a comprehensive discussion of some explicitly chosen methods in depth. Therefore, the students will not only learn how to process two dimensional signals but also gain a deep insight into applied mathematics and computer science. The developed mathematical and numerical understanding will not only allow to understand the beauty of medical image processing but also supply the students with a basic understanding of mathematical methods, which indeed is of great value in many other fields. In the very beginning of the lecture the students will be supplied with the basics of the representation of digital images. Subsequently several methods used for feature extraction, contrast enhancement and filtering in the spatial as well as the Fourier domain will be explained. These very lectures form the basis for the understanding of higher image processing methods such as image restauration, image segmentation and image registration, all of which to date still represent a highly active field of research with many unsolved problems. The lecture will not only focus on the theoretical background but will be accompanied with illustrative examples to further develop an understanding of the theoretically presented mathematical and numerical methods.

Recommended Literature:

• Rafael C. Gonzales, Digital Image Processing, Prentice Hall, New Jersey, 2008.
• Bernd Jähne, Digital Image Processing, Springer, Berlin Heidelberg, 2002.
• Thomas Lehmann et al., Bildverarbeitung für die Medizin, Springer, Berlin Heidelberg, 1997.
• William K. Pratt, Digital Image Processing: PIKS Scientific Inside, John Wiley & Sons, 2007.

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