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Proceedings Paper

Automated vertebra identification in CT images
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Paper Abstract

In this paper, we describe and compare methods for automatically identifying individual vertebrae in arbitrary CT images. The identification is an essential precondition for a subsequent model-based segmentation, which is used in a wide field of orthopedic, neurological, and oncological applications, e.g., spinal biopsies or the insertion of pedicle screws. Since adjacent vertebrae show similar characteristics, an automated labeling of the spine column is a very challenging task, especially if no surrounding reference structures can be taken into account. Furthermore, vertebra identification is complicated due to the fact that many images are bounded to a very limited field of view and may contain only few vertebrae. We propose and evaluate two methods for automatically labeling the spine column by evaluating similarities between given models and vertebral objects. In one method, object boundary information is taken into account by applying a Generalized Hough Transform (GHT) for each vertebral object. In the other method, appearance models containing mean gray value information are registered to each vertebral object using cross and local correlation as similarity measures for the optimization function. The GHT is advantageous in terms of computational performance but cuts back concerning the identification rate. A correct labeling of the vertebral column has been successfully performed on 93% of the test set consisting of 63 disparate input images using rigid image registration with local correlation as similarity measure.

Paper Details

Date Published: 27 March 2009
PDF: 11 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590B (27 March 2009); doi: 10.1117/12.811836
Show Author Affiliations
Matthias Ehm, Philips Research Europe (Germany)
Tobias Klinder, Philips Research Europe (Germany)
Leibniz Univ. of Hannover (Germany)
Reinhard Kneser, Philips Research Europe (Germany)
Cristian Lorenz, Philips Research Europe (Germany)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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