Keywords: Machine Learning for Medical Applications
Many medical applications such as registration or tracking can be seen as the optimization of an objective function which involves a data term or similarity measure. Classical similarity measures rely for instance on image intensities, gradients or intensity statistics. In the case of noise or background clutter which is very frequent in the case of medical imaging, they might lead to registration/tracking errors. In this project, we investigate different approches and applications of learning a similarity measure directly from the data, leading to a more robust data term which is adapted to the image characteristics.
||O. Pauly, H. Heibel, N. Navab
A Machine Learning Approach for Deformable Guide-Wire Tracking in Fluoroscopic Sequences.
Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Beijing, China, September 2010
||O. Pauly, N. Padoy, H. Poppert, L. Esposito, H-H. Eckstein, N. Navab
Towards Application-specific Multi-modal Similarity Measures: a Regression Approach.
MICCAI Workshop on Probabilistic Models in Medical Image Analysis (PMMIA), London, UK, September 2009.
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Please contact Olivier Pauly for available student projects within this research project.