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.