| Course Code : | 2022FBDBMW | | Study domain: | Physics | | Semester: | Semester: 2nd semester
| | Contact hours: | 55 | | Credits: | 6 | | Study load (hours): | 168 | | Contract restrictions: | Exam contract not possible
| | Language of instruction : | Dutch
| | Exam period: | exam in the 2nd semester
| | Tutor(s) | Jan Sijbers
Dirk Van Dyck
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1. Prerequisites
At the start of this course the student should have acquired the following competences:
- Competences corresponding the final attainment level of secondary school
An active knowlegde of :Specific prerequisites for this course:
Basic knowledge of mathematical techniques (bachelor level) is required.
2. Learning outcomes
To impart knowledge and understanding of analysis of signals and systems when considered in the time and frequency domains, and to enable the student to formally analyse systems through the use of spectral analysis and correlations. The student will also be able to take account of the effects of sampling in the time and frequency domain and understand how these affect the practical analysis procedures. The students will be able to select the appropriate infinite or finite impulse response digital filter and undertake the design of the filter coefficients. The student should gain a familiarity with the derivation of the fast Fourier transform (FFT) algorithm and with its computational advantages.
The objective of image processing part is to provide students with an overview of the computational and mathematical methods in biomedical image processing. The course covers the main sources of medical imaging data (CT, MRI, PET, and ultrasound). We will study many of the current methods used to enhance and extract useful information from biomedical images.
The student should be able to:
Explain the relationships between and be able to manipulate time domain and frequency domain representations of signals. Apply correlation techniques to an analytic or numerical problem, and relate the outcome to the properties of the signal source(s). - recall how the discrete Fourier transform arises and recognise the effect of resolution and windowing functions upon the discrete Fourier transform;
- derive the structure of the fast Fourier transform from the equation of the discrete Fourier transform
- analyse the effects of downsampling and upsampling on a signal and recognise the importance of decimation and interpolation filtering.
- Basic concepts for digital images, e.g. pixel, grey level, histogram, frame.
- Linear filtering, including smoothing, sharpening and edge detection.
- Detection of image structures
- Image processing in MatLab
3. Course contents
The following chapters will be discussed:
- signals and systems
- time domain description and convolution
- LTI system and its properties
- Fourier transformations
- Laplace transform
- Sampling
- Z-transform
- Filter design
- wavelet transform
4. Teaching method
Class contact teaching: LecturesPractice sessionsLaboratory sessions
5. Assessment method and criteria
Examination: Written with oral presentationOral with written preparationClosed bookPractical examination
6. Study material
Required reading
The course material that is provided: text + slides.
Highly recommended:
Zhi-Pei Liang and Paul Lauterbur, Principles of Magnetic Resonance Imaging: A Signal Processing Perspective, IEEE Press Series in Biomedical Engineering
Optional reading
The following study material can be studied on a voluntary basis:
A. V. Oppenheim and R. W. Schafer and J. R. Buck, “Discrete-time Signal Processing”, Prentice-Hall, Second Ed., 1999.
7. Contact information
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Prof. Dr. Jan Sijbers
Universiteitsplein 1, B-2610 Wilrijk, Belgium
Tel: +32 3 265 24 64 Fax: +32 3 265 22 45
Elm: jan.sijbers@ua.ac.be
www: http://visielab.ua.ac.be/staff/sijbers
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(+)last update: 12/10/2012 18:06 jan.sijbers
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