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Biomedical Signal & Image Processing

Module name (EN):
Name of module in study programme. It should be precise and clear.
Biomedical Signal & Image Processing
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Neural Engineering, Master, ASPO 01.04.2020
Module code: NE2102.SIP
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P213-0135
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+3P (5 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 1
Mandatory course: yes
Language of instruction:
English
Assessment:
Oral exam

[updated 12.03.2020]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

NE2102.SIP (P213-0135) Neural Engineering, Master, ASPO 01.04.2020 , semester 1, mandatory course
NE3102.SIP Neural Engineering, Master, SO 01.10.2025 , semester 1, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
75 class hours (= 56.25 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 123.75 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Dr. Daniel Strauß
Lecturer:
Prof. Dr. Dr. Daniel Strauß


[updated 12.03.2020]
Learning outcomes:
To develop skills for analyzing and synthesizing algorithms and systems that process discrete time sequences and images, with a particular emphasis on biomedical applications. Topics include time-frequency analysis, filter banks, and hierarchical decompositions. This course covers the theory, practical implementation techniques, and intense hands-on exercises in the lab to sharpen computing and implementation skills. It provides students with the ability to process biomedical signals and images for denoising, registration, segmentation, and feature extraction purposes. Adopting current challenges in medical decision making and decision support system design, the course provides students with a good understanding of the importance of feature extraction in biomedical pattern recognition schemes; a prerequisite for pattern recognition architectures in NE2105 "Neural & Cognitive Systems".
 
Participation in actual research studies and/or lab projects to complement course topics is required. That way, students receive soft skill training related to safe patient handling.

[updated 18.07.2019]
Module content:
1.        Time-Frequency Analysis
1.1.        Basics of Hilbert Space Theory
1.2.        Windowed Fourier Transform
1.3.        Continuous Wavelet Transform
1.4.        Multiscale-Analysis
1.5.        Hardy Space Projections & Analytic Signals
1.6.        Generalized Time-Frequency Distributions
 
2.        Multirate Filterbanks
2.1. Basic Operations
2.2. Polyphase Representations
2.3. Two-Channel Filter Banks  
2.4. Perfect Reconstruction Filter Banks
2.5. Lattice Structure Implementation
 
3. Signal Decompositions
3.1. Orthonormal Bases via Paraunitary Filter Banks
3.2. Tree Decompositions of l^2(Z)
3.3. Frames & Oversampled Filter Banks
3.4. Design of Signal-Adapted Filters & Filter Banks
3.5. Adaptive Feature Extraction using Filter Banks
3.6. Implementation Architectures (DSPs, FPGAs, ASICs)
 
4. Hierarchical Image Processing
4.1. Review: Vector Calculus, PDEs, Finite Difference Method
4.2. Basic Operations & Filters
4.3. Diffusion and Total Variation Filtering
4.4. Multiscale Image Representations
4.5. Optic/Scene Flow Concepts
4.6. Registration, Segmentation, and Denoising
4.7. CPU/GPU/DSP Implementations

[updated 18.07.2019]
Teaching methods/Media:
Lecture notes, digital projector, software in the PC lab

[updated 12.03.2020]
Recommended or required reading:
Bruce, Eugene N.: Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2001
Daubechies, I.: Ten Lectures on Wavelets, SIAM, 1992
Gonzalez, Rafael C.; Woods, Richard E.: Digital Image Processing, Pearson, (akt. Aufl.)
Oppenheim, Alan V., Schafer, Ronald W., Buck, John R.: Discrete-time signal processing, Prentice-Hall, 1999, 2nd Ed.
Parker, J. R.: Algorithms for Image Processing and Computer Vision, Wiley, 2010, 2nd Ed., ISBN 978-0470643853
Preim, Bernhard; Botha, Charl P.: Visual Computing for Medicine, Morgan Kaufmann, 2013, 2rd Ed., ISBN 978-0124158733
Price, Kenneth V.; Storn, Rainer; Lampinen, Jouni A.: Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005
Schölkopf, B.; Smola, A.J.: Learning with Kernels: Support vector Machinces, Regularization, Optimization and Beyond, MIT Press, 2002
Semmlow, John L.: Biosignal and Biomedical Image Processing, Marcel Dekker, 2004
Sonka, Milan; Hlavac, Vaclav; Boyle, Roger: Image Processing, Analysis, and Machine Vision, Cengage Learning, 2014, ISBN 978-1133593690
Suh, Jung W.; Kim, Youngmin: Accelerating MATLAB with GPU Computing: A Primer with Examples, Morgan Kaufmann, 2013, ISBN 978-0124080805
Vetterli, Martin; Kovacevic, Jelena: Wavelets and Subband Coding, Prentice Hall, 1995
Welch, Thaddeus B.; Wrigth, Cameron H.G:, Morrow, Michael G.: Real-Time Digital Signal Processing from MATLAB to C with the TMS320C6x DSPs, CRC Press, 2017, 3rd Ed., ISBN 978-1498781015
Woods, Roger; McAllister, John; Lightbogy, Gaye; Yi, Ying: FPGA-based Implementation of Signal Processing Systems, Wiley, 2017, 2nd Ed., ISBN 978-1119077954

[updated 18.07.2019]
 
[Fri Dec 27 02:22:45 CET 2024, CKEY=nemNE2102.SIP, BKEY=nem, CID=NE2102.SIP, LANGUAGE=en, DATE=27.12.2024]