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.
%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
}