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TU Berlin

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MRI, EM, Autoradiography, and Multi-modal Data


Here we combine techniques from machine learning, from image processing, and from signal processing to analyse biomedical data from various domains. Currently, applications include the 3D-reconstruction and visualization of brain structures, the segmentation and interpretation of the imaging data, and the correlation of anatomical with functional imaging data and with genetic data. Datasets and analysis problems also serve as a testbed for algorithms which we have developed in our machine learning projects (cf. "Research" page Learning on Structured Representations).

Acknowledgement: Research was funded by the DFG, the BMBF and the
Technische Universität Berlin.

Selected Publications:

Puls, I., Mohr, J., Wrase, J., Vollstaedt-Klein, S., Lemenager, T., Vollmert, C., Rapp, M., Obermayer, K., Heinz, A. and Smolka, M. (2009). A Model Comparison of COMT Effects on Central Processing of Affective Stimuli. Neuroimage, 46, 683 – 691.

Puls, I., Mohr, J., Wrase, J., Priller, J., Behr, J., Kitzrow, W., Makris, N., Breiter, H., Obermayer, K. and Heinz, A. (2008). Synergistic Effects of the Dopaminergic and Glutamatergic System on Hippocampal Volume in Alcohol-Dependent Patients. Biological Psychology, 79, 126 - 136.

Gaudnek, M., Hess, A., Obermayer, K., Budinsky, L., Brune, K. and Sibila, M. (2005). Geometric Reconstruction of the Rat Vascular System Imaged by MRA. IEEE International Conference on Image Processing 2005. IEEE, 1278-1281.,10.1109/ICIP.2005.1530296

Mohr, J., Hess, A., Scholz, M. and Obermayer, K. (2004). A Method for the Automatic Segmentation Autoradiographic Image Stacks and Spatial Normalization of Functional Cortical Activity Data. J. Neurosci. Methods, 134, 45 – 58.

Vollgraf, R., Scholz, M., Meinertzhagen, I. and Obermayer, K. (2004). Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression. Advances in Neural Information Processing Systems 16. MIT Press, 717 – 724.,

Pielot, R., Scholz, M., Obermayer, K., Scheich, H., Gundelfinger, E. and Hess, A. (2003). A New Point-Based Warping Method for Enhanced and Simplified Analysis of Functional Brain Image Data. Neuroimage, 19, 1716 – 1729.

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