@danielkottke

Data-Driven Spine Detection for Multi-Sequence MRI

, , and . Bildverarbeitung für die Medizin (BVM2015), Springer Berlin Heidelberg, (2015)

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.

Links and resources

DOI:
10.1007/978-3-662-46224-9_3
URL:
BibTeX key:
KottkeEtAl2015BVM
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