Oliver Gloger
University of Greifswald
13 Papers
40 Citations
Oliver Gloger is an academic researcher from University of Greifswald. The author has contributed to research in topics: Segmentation & Scale-space segmentation. The author has an hindex of 7, co-authored 13 publications.
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Papers
Cohort Profile: The Study of Health in Pomerania
Henry Völzke,Dietrich Alte,Carsten Oliver Schmidt,Dörte Radke,Roberto Lorbeer,Nele Friedrich,Nicole Aumann,Katharina Lau,Michael Piontek,Gabriele Born,Christoph Havemann,Till Ittermann,Sabine Schipf,Robin Haring,Robin Haring,Sebastian E. Baumeister,Henri Wallaschofski,Matthias Nauck,Stephanie Frick,Andreas Arnold,Michael Jünger,Julia Mayerle,Matthias Kraft,Markus M. Lerch,Marcus Dörr,Thorsten Reffelmann,Klaus Empen,Stephan B. Felix,Anne Obst,Beate Koch,Sven Gläser,Ralf Ewert,Ingo Fietze,Thomas Penzel,Martina Dören,Wolfgang Rathmann,Johannes Haerting,Mario Hannemann,J Röpcke,Ulf Schminke,Clemens Jürgens,Frank Tost,Rainer Rettig,Jan A. Kors,Saskia Ungerer,K Hegenscheid,Jens Peter Kühn,Julia Kühn,Norbert Hosten,Ralf Puls,Jörg Henke,Oliver Gloger,Alexander Teumer,Georg Homuth,Uwe Völker,Christian Schwahn,Birte Holtfreter,Ines Polzer,Thomas Kohlmann,Hans J. Grabe,Dieter Rosskopf,Heyo K. Kroemer,Thomas Kocher,Reiner Biffar,Ulrich John,Wolfgang Hoffmann +65 more
TL;DR: Henry Volzke, y Dietrich Alte,1y Carsten Oliver Schmidt, Dorte Radke, Roberto Lorbeer, Nele Friedrich, Nicole Aumann, Katharina Lau, Michael Piontek, Gabriele Born, Christoph Havemann, Till Ittermann, Sabine Schipf, Robin Haring, Sebastian E Baumeister, Henri Wallaschofski, Matthias Nauck, Stephanie Frick, Andreas Arnold.
A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.
TL;DR: This work develops a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique that is modularized and can be applied for normal and fat accumulated liver tissue properties.
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Prior Shape Level Set Segmentation on Multistep Generated Probability Maps of MR Datasets for Fully Automatic Kidney Parenchyma Volumetry
TL;DR: A 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation and is able to recognize and exclude parenchymal cysts from the paren chymal volume.
61
Fully Automated Glottis Segmentation in Endoscopic Videos Using Local Color and Shape Features of Glottal Regions
TL;DR: A fully automatedglottis segmentation framework that extracts a set of glottal regions in endoscopic videos by using a flexible thresholding technique combined with a refining level set method that incorporates prior glottis shape knowledge.
40
Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data
TL;DR: A modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies.
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