Module ME3000-KP08, ME3000SJ14

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

8

Course of studies, specific fields and terms:

• Bachelor MES 2020, compulsory, medical engineering science
• Bachelor MES 2014, compulsory, medical engineering science

Classes and lectures:

• CS3310-V: Medical Image Computing (lecture, 2 SWS)
• ME3100-Ü: Medical Imaging (exercise, 1 SWS)
• ME3100-V: Medical Imaging (lecture, 2 SWS)
• CS3310-Ü: Medical Image Computing (exercise, 2 SWS)

Workload:

• 110 hours private studies
• 90 hours in-classroom work
• 40 hours exam preparation

Contents of teaching:

• Introduction to the theory of imaging systems
• Ultrasound imaging
• Conventional X-ray imaging, Computed Tomography
• Magnetic Resonance Imaging
• Motivation, principles and applications of medical image computing
• Structure and formats of medical images
• Histograms and image transformations
• Image filtering using Fourier transform
• Image filtering with local operators
• Segmentation: thresholding, region growing
• Clusteranalysis and classifyer for image segmentation
• Introducing convolutional neural networks
• Morphological operators
• Application and evaluation of segmentation methods
• Image interpolation methods and transformataion of images
• Basic methods of image registration
• Combined signal and image analysis in 4D image processing

Qualification-goals/Competencies:

• The students can characterise linear translation-invariant imaging systems by means of impulse response and transfer function.
• They can explain the Nyquist-Shannon theorem and justify its validity.
• They can describe what is meant by spatial resolution of an imaging system.
• They can give an overview of important medical imaging techniques.
• They can explain the physical foundations of ultrasound imaging.
• They can describe the behaviour of ultrasound waves at tissue borders.
• They can reason the fundamental limit to spatial resolution in US.
• They can list the interdependence between ultrasound frequency, spatial resolution, and penetration depth.
• They can elucidate how technical parameters are chosen for a given target to be imaged.
• They can discuss aim and realisation of beam forming in US imaging.
• They can explain how Doppler US works.
• They can describe why important US image artefacts occur.
• They can explain the physical and technical foundations of X-ray generation.
• They can sketch the typical spectrum of a technical X-ray source.
• They can list and describe the most important interaction processes between X-rays and matter.
• They can mention possible sources of hazard in X-ray imaging and discuss strategies for avoiding them.
• They can describe the influence of technical parameters in X-ray imaging systems.
• They can describe and justify important reconstruction principles in CT and their mathematical foundations.
• They can explain the physical foundations of nuclear magnetic resonance (NMR).
• They can describe how spatial resolution is achieved in NMR imaging.
• They can justify the occurrence of different types of radio frequency echoes in NMR.
• They can explain the concept of k-space.
• They can describe how different weightings are achieved in MR images.
• They can list sources of hazard in MRI and explain their causes.
• They can describe the technical components of an MR imaging system.
• They can implement important algorithms used in imaging systems.
• Students are able to classify basic medical image processing methods, are able to characterize them and to apply them to concrete problems.
• They are able to select appropriate, problem-specific methods for image filtering, image segmentation, and morphological post-processing of segmentation results, to combine them in a processing pipeline and to use them for image enhancement or image segmentation of medical structures.
• They are able to distinguish between different methods of cluster analysis and statistical and neural pattern recognition and can characterize them based on different implicitly used model assumptions and properties.
• They are able to evaluate segmentation results of different methods based on established quality measures and to carry out an objective comparison of the quality of different segmentation methods in practical use.
• They are able to distinguish between different image interpolation methods, to classify them according to their specific advantages and disadvantages and to select an appropriate method and apply it, depending on a specific problem.
• They are able to assess the characteristics of different rigid image registration methods. For a specific registration problem they are able to select problem specific similarity measures and regularization terms and to parameterize them.
• They are able to distinguish and to characterize different techniques for analyzing functional 4D fMRI image sequences, with whom neurally activated brain areas in 4D image sequences of the head can be made visible.
• They are able to implement basic image processing algorithms and to bring them to use in combination with medical image processing modules available from program libraries.

Grading through:

• written exam

Responsible for this module:

• Prof. Dr. rer. nat. Martin Koch

Teacher:

• Institute of Medical Engineering
• Institute of Medical Informatics
• Prof. Dr. rer. nat. habil. Heinz Handels
• Prof. Dr. rer. nat. Martin Koch

Literature:

• O. Dössel : Bildgebende Verfahren in der Medizin Springer, Berlin 2000
• H. Morneburg (Hrsg.) : Bildgebende Systeme für die medizinische Diagnostik. 3. Aufl. Publicis MCD Verlag, München 1995
• H. Handels : Medizinische Bildverarbeitung Stuttgart: Vieweg & Teubner 2009
• T. Lehmann : Handbuch der Medizinischen Informatik München: Hanser 2004
• M. Sonka, V. Hlavac, R. Boyle : Image Processing, Analysis and Machine Vision 2nd edition. Pacific Grove: PWS Publishing 1998

Language:

• German and English skills required

Notes:

Last Updated:

06.12.2024