Duration:
1 Semester | Turnus of offer:
each winter semester | Credit points:
8 |
Course of studies, specific field and terms: - Bachelor Medical Informatics 2014 (compulsory), medical computer science, 5th semester
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Classes and lectures: - Practical Medical Image Computing (practical course, 1 SWS)
- Medical Image Computing (lecture, 2 SWS)
- Practical Medical Image Computing (exercise, 1 SWS)
- Practical Medical Image Computing (lecture, 1 SWS)
- Medical Image Computing (exercise, 1 SWS)
| Workload: - 90 Hours in-classroom work
- 40 Hours private studies
- 20 Hours exam preparation
- 30 Hours group work
- 50 Hours work on project
- 10 Hours oral presentation (including preparation)
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Contents of teaching: | - Motivation, principles and applications of medical image computing
- Structure and formats of medical images
- Fundamentals of pattern recognition (segmentation, feature extraction, classification, interpretation)
- Histograms and image transformations
- Image filtering using Fourier transform
- Image filtering with local operators
- Segmentation: thresholding, region growing
- Clusteranalysis and classifyer for image segmentation
- Morphological operators
- Application and evaluation of segmentation methods
- Image interpolation methods
- Transformataion of images
- Basic methods of image registration: rigid image registration
- Combined signal and image analysis in 4D image processing
- Application examples
- INTERNSHIP:
- Introduction to the methods and software tools required for the project internship
- Planning and implementation of a complete software project in group work based on division of labour while adhering to standards and deadlines
- The project topics to be worked on are chosen from the field of medical image processing using clinical image data
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Qualification-goals/Competencies: - 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 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.
- They have the ability to develop problem-specfic medical image analysis systems by using various software tools.
- In this context, they are able to analyze complex tasks, to break them down into sub-tasks and to implement them in teams.
- They have the ability to estimate the project effort, to plan the project schedule and to use resources appropriately.
- They can document the developed solutions and present the results.
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Grading through: - successful addressing of the project goals
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Is requisite for: |
Responsible for this module: Teachers: |
Literature: - 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
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Language: |
Notes:Admission requirements for taking the module:
- None (the competences of the module mentioned under 'Requires' are neede for this module, but are not a formal prerequisite)
Admission requirements for participation in module examination(s):
- Module part 'Medical Image Computing' (V+Ü): Successful completion of exercise sheets as specified at the beginning of the semester
- Module part 'Internship in Medical Image Computing': Regular participation in the practical course as specified at the beginning of the semester
Module Exam(s):
- CS3310-L1: Medical Image Computing, written exam, 60min, 50% of the module grade
-CS3310-L2: Internship Medical Image Computing, graded Internship, 50% of the module grade |
Letzte Änderung: 11.3.2024 |
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