FYSS6430 Röntgen Tomography and Image Analysis (4 cr)
Description
Structure and operation of X-ray tomographic devices
Image reconstruction and artifact reduction
Basics of digital image processing, noise removal, segmentation, visualization
Image-based measurements and uncertainty analysis
Mathematical methods present in the course: (briefly reviewed) Fourier transform, convolution, (to be introduced) Radon transform, (is present) matrix calculus, discrete/numerical differentiation, differential equations, statistical/probability distributions and their estimators.
Learning outcomes
At the end of this course,
students will be able to explain how X-ray tomographic devices function as well as use such devices.
Students will be able to perform tomographic reconstruction and analyze three-dimensional images with suitable tools and algorithms.
Students will also be able to plan image-based measurements and evaluate uncertainty in the measurements.
Description of prerequisites
The student knows how to use statistical analysis to present data and a computer software meant for numerical calculation (for example MATLAB).
Basics of integral and differential calculus, integral transforms and linear algebra
Study materials
Lecture notes
Literature
- Microcomputed tomography – Methodology and Applications, by Stuart R. Stock
- Digital image processing, by Rafael Gonzalez and Richard E. Woods
Completion methods
Method 1
Method 2
Teaching (4 cr)
Lectures, math and image analysis exercises and practice sessions, laboratory work and short presentation from laboratory report.
Teaching
1/10–3/2/2023 Lectures
Independent study (4 cr)
Contact appointments, exercises, laboratory work and evaluation discussion of the laboratory report.