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Publications

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Filters: Author is B. Michael Kelm  [Clear All Filters]
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Kelm, B Michael (2007). Evaluation of Vector-Valued Clinical Image Data Using Probabilistic Graphical Models: Quantification and Pattern Recognition. University of HeidelbergPDF icon Technical Report (4.89 MB)
Kelm, B Michael, Kaster, F O, Henning, A, Weber, M - A, Bachert, P, Bösinger, P, Hamprecht, F A and Menze, B H (2011). Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images. NMR in Biomedicine. 25(1) 1-13PDF icon Technical Report (1.94 MB)
Kelm, B Michael, Müller, N, Menze, B H and Hamprecht, F A (2006). Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM. Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis). IEEE Computer Society. 96-103PDF icon Technical Report (232.69 KB)
Kelm, B Michael, Menze, B H and Hamprecht, F A (2005). Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen. VDI-Berichte. 1883 457-466PDF icon Technical Report (221.54 KB)
Kelm, B Michael, Menze, B H, Neff, T, Zechmann, C M and Hamprecht, F A (2006). CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.. Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen. Springer. 51-55. http://www.efmi-wg-mip.net/service/bvm2006PDF icon Technical Report (275.25 KB)
Kelm, B Michael, Menze, B H, Nix, O, Zechmann, C M and Hamprecht, F A (2009). Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge. IEEE Transaction on Medical Imaging. 28:10 1534-1547PDF icon Technical Report (419.8 KB)
Kelm, B Michael, Menze, B H, Zechmann, C M, Baudendistel, K T and Hamprecht, F A (2007). Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification. Magnetic Resonance in Medicine. 57 150-159PDF icon Technical Report (348.05 KB)
Kelm, B Michael, Pal, C and McCallum, A (2006). Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.. ICPR 2006. 2 828-832PDF icon Technical Report (114.99 KB)