Medical Image Processing
- The course cannot be taken simultaneously with:
- Medical Image Processing (BAM33ZMO)
Medical Imaging Systems 1 (X33ZS1)
- The course is a substitute for:
- Medical Imaging Systems 1 (X33ZS1)
- Department of Cybernetics
This subject describes algorithms for digital image processing of 2D and 3D images, with emphasis on biomedical applications. We shall therefore concentrate on the most often used techniques in medical image processing: segmentation, registration, and classification. The methods will be illustrated by a range of examples on medical data. The students will implement some of the algorithms during the practice sessions.
Because of the very large overlap between courses A6M33ZMO and A4M33ZMO, the courses will be taught together this year.
The knowledge of basic signal processing methods including a Fourrier transform, and the knowledge of the basic principles of medical imaging methods.
- Syllabus of lectures:
Because of the large overlap between courses A6M33ZMO and A4M33ZMO, the courses will be taught together this year.
1. Introduction, digitalization and quantization, intensity transformations, histogram.
2. Interpolation, geometric transformation, 2D/3D linear and non-linear filtering.
3. Noise suppression, Wiener filtering, wavelet filtering and de-noising.
4. Mathematical morphology. Texture and its description.
5. Segmentation, low-level methods (thresholding, region growing).
6. Graph-based segmentation methods.
7. Active contours. Principle component analysis and statistical shape and appearance models.
8. Levelset based segmentation.
9. Registration based on landmarks, elastic and rigid, robust methods.
10. Similarity criteria, optimization methods for elastic registration.
11. Discrete registration methods. Diffeomorphic registration methods. Optical flow.
12. The reconstruction problem with applications to CT, regularization, non-linear methods.
13. Detection and classification, applications in mamography, CT, MRI and ultrasound.
- Syllabus of tutorials:
Individual works will consist of independent practical work involving the use of algorithms covered by the course for analysis of specific medical data.
- Study Objective:
Learn the principles and usage of basic algorithms for medical image processing, such as registration, segmentation and classification. The students will learn to implement some of the algorithms.
- Study materials:
 Sonka M., Fitzpatrick J. M.: Handbook of Medical Imaging, vol.2. SPIE Press, 2000.
 Bankman, I. Handbook of Medical Imaging, Processing and Analysis, vol.1. Academic Press, 2000.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: