Hierarchical segmentation of vertebrae from x-ray images

The problem of vertebrae segmentation in digitized x-ray images is addressed with a hierarchical approach that combines three different methodologies. As a starting point, two customized active shape models are trained on data sets of cervical and lumbar images, respectively. Here, a methodology to include edge information in the gray-level modeling part of the active shape models is developed to increase the representativeness of the model and to improve the chances of finding vertebral boundaries. Active shape models' initialization shortcoming is then addressed by a customized implementation of the Generalized Hough Transform, which provides an estimate of the pose of the vertebrae within target images. Active shape models' shortcoming of lack of local deformation is addressed by a customized implementation of the technique of Deformable Models. In this implementation, an energy minimization approach is employed in which the external energy term is extracted from the training set of images and the internal energy terms control the shape of the template. Segmentation results on data sets of cervical and lumbar images show that the proposed hierarchical approach produces errors of less than 3mm in 75% of the cervical images and 6.4mm in 50% of the lumbar images.