%0 Book Section %1 KottkeEtAl2015BVM %A Kottke, Daniel %A Gulamhussene, Gino %A Tönnies, Klaus %B Bildverarbeitung für die Medizin (BVM2015) %D 2015 %E Handels, Heinz %E Deserno, Thomas Martin %E Meinzer, Hans-Peter %E Tolxdorff, Thomas %I Springer Berlin Heidelberg %K curved detection imaging mri planar reconstruction registration regression spine vertebra %P 5-10 %R 10.1007/978-3-662-46224-9_3 %T Data-Driven Spine Detection for Multi-Sequence MRI %U http://dx.doi.org/10.1007/978-3-662-46224-9_3 %X Epidemiology studies on vertebra’s shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRl). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90% of all spines correctly, and present a preliminary analysis of vertebrae sizes. %@ 978-3-662-46223-2

@incollection{KottkeEtAl2015BVM, abstract = {Epidemiology studies on vertebra’s shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRl). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90% of all spines correctly, and present a preliminary analysis of vertebrae sizes.}, added-at = {2015-04-07T11:49:19.000+0200}, author = {Kottke, Daniel and Gulamhussene, Gino and Tönnies, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/20862610f972011992b8f6f48f2f0a40b/danielkottke}, booktitle = {Bildverarbeitung für die Medizin (BVM2015)}, doi = {10.1007/978-3-662-46224-9_3}, editor = {Handels, Heinz and Deserno, Thomas Martin and Meinzer, Hans-Peter and Tolxdorff, Thomas}, interhash = {daf2de716a544810744378298b345d06}, intrahash = {0862610f972011992b8f6f48f2f0a40b}, isbn = {978-3-662-46223-2}, keywords = {curved detection imaging mri planar reconstruction registration regression spine vertebra}, language = {English}, pages = {5-10}, publisher = {Springer Berlin Heidelberg}, series = {Informatik aktuell}, timestamp = {2015-04-08T10:45:16.000+0200}, title = {Data-Driven Spine Detection for Multi-Sequence MRI}, url = {http://dx.doi.org/10.1007/978-3-662-46224-9_3}, year = 2015 }