Similarity/Metric/Distance Learning for Medical Applications Scientific Director: Nassir Navab Contact Person(s): Olivier Pauly

Keywords: Machine Learning for Medical Applications

Abstract

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.

Publications

2010
	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 (bib)
2009
	O. Pauly, N. Padoy, H. Poppert, I. 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. (bib)

Team

Contact Person(s)

Dr. Olivier Pauly

Working Group

Prof. Diana Mateus

Prof. Dr. Nassir Navab

Dr. Olivier Pauly

Alumni

Prof. Nicolas Padoy

Location

Technische Universität München Institut für Informatik / I16 Boltzmannstr. 3 85748 Garching bei München Tel.: +49 89 289-17058 Fax: +49 89 289-17059

Visit our lab at Garching.



internal project page

Please contact Olivier Pauly for available student projects within this research project.