Computer Aided Medical Procedures II - IN2022
Lecture: Prof. Nassir Navab
Organization: Dr. Guillaume Zahnd
and Dr. Ulrich Eck
and Dr. Seong Tae Kim
Time & Location
Upon successful completion of the module participants are able to understand the fundamentals, differences, and application areas of advanced methods for image processing, image segmentation and image registration as well as are able to implement them in Python. Moreover, participants are able to understand the fundamentals of machine learning and 3D volume visualization. Furthermore, participants are able to understand complex problems in the area of computer aided diagnosis and interventions as well as to develop solution strategies based on the covered algorithms in the aforementioned areas.
- Basic knowledge in image computing (not necessarily acquired through the CAMP I lecture)
- Interest in algorithmic/implementation aspects
- Specific knowledge in Python is not required but beneficial. However, you should be somehow skilled in programming.
- Module Spezification: IN2022
- SWS: 2+2
- ECTS: 5 Credits
- Course Language: English
- Exam: 90 minutes, written, closed book
-  MATLAB-Python conversion
-  Rudin, L. I., Osher, S., Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1-4), 259-268.
-  Chan, T. F., Vese, L. A. (2001). Active contours without edges. IEEE Transactions on image processing, 10(2), 266-277.
-  Chan, T. F., Esedoglu, S., Nikolova, M. (2006). Algorithms for finding global minimizers of image segmentation and denoising models. SIAM journal on applied mathematics, 66(5), 1632-1648.
-  Thirion, J. P. (1998). Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis, 2(3), 243-260.
-  Bro-Nielsen, M., & Gramkow, C. (1996). Fast fluid registration of medical images. In Visualization in Biomedical Computing (pp. 265-276). Springer Berlin Heidelberg.
-  Vercauteren, T., Pennec, X., Perchant, A., & Ayache, N. (2009). Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage?, 45(1), S61-S72.
-  Zikic, D., Baust, M., Kamen, A., & Navab, N. (2011, November). A general preconditioning scheme for difference measures in deformable registration. In Computer Vision (ICCV), 2011 IEEE International Conference on (pp. 49-56). IEEE.
-  Myronenko, A., & Song, X. (2010). Point set registration: Coherent point drift. IEEE transactions on pattern analysis and machine intelligence, 32(12), 2262-2275.
-  Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
-  Machine Learning with MATLAB (2017)
-  A. Vedaldi, Image Representations, from shallow to deep (2014)