Mr. Matthias Ehm Profile

Matthias Ehm

at Philips Research

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Proceedings Article | 27 March 2009 Paper

Automated vertebra identification in CT images

Matthias Ehm, Tobias Klinder, Reinhard Kneser, Cristian Lorenz

Proceedings Volume 7259, 72590B (2009) https://doi.org/10.1117/12.811836

KEYWORDS: Spine, Image segmentation, Computed tomography, Image registration, Data modeling, Systems modeling, Hough transforms, Image processing, Rigid registration, Virtual colonoscopy

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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.

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