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B. Michael Kelm, “Evaluation of Vector-Valued Clinical Image Data Using Probabilistic Graphical Models: Quantification and Pattern Recognition”. University of Heidelberg, 2007.PDF icon Technical Report (4.89 MB)
B. Michael Kelm, Kaster, F. O., Henning, A., Weber, M. - A., Bachert, P., Bösinger, P., Hamprecht, F. A., and Menze, B. H., “Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images”, NMR in Biomedicine, vol. 25(1), pp. 1-13, 2011.PDF icon Technical Report (1.94 MB)
B. Michael Kelm, Müller, N., Menze, B. H., and Hamprecht, F. A., “Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM”, in Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis), 2006, pp. 96-103.PDF icon Technical Report (232.69 KB)
B. Michael Kelm, Menze, B. H., and Hamprecht, F. A., “Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen”, in VDI-Berichte, 2005, vol. 1883, pp. 457-466.PDF icon Technical Report (221.54 KB)
B. Michael Kelm, Menze, B. H., Neff, T., Zechmann, C. M., and Hamprecht, F. A., “CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.”, in Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen, 2006, pp. 51-55.PDF icon Technical Report (275.25 KB)
B. Michael Kelm, Menze, B. H., Nix, O., Zechmann, C. M., and Hamprecht, F. A., “Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge”, IEEE Transaction on Medical Imaging, vol. 28:10, pp. 1534-1547, 2009.PDF icon Technical Report (419.8 KB)
B. Michael Kelm, Menze, B. H., Zechmann, C. M., Baudendistel, K. T., and Hamprecht, F. A., “Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification”, Magnetic Resonance in Medicine, vol. 57, pp. 150-159, 2007.PDF icon Technical Report (348.05 KB)
B. Michael Kelm, Pal, C., and McCallum, A., “Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.”, in ICPR 2006, 2006, vol. 2, pp. 828-832.PDF icon Technical Report (114.99 KB)