Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit

A computer-assisted system that can automatically provide rapid localization and accurate labeling of vertebral disks and bodies is a highly desirable tool due to the large demand for the diagnostic imaging and surgical planning of the vertebral column structures. However, a reliable detection and definitive labeling of vertebrae can be difficult due to factors such as the limited imaging coverage and various vertebral anomalies particularly in the thoracolumbar and lumbosacral junctions. In this paper, we investigate the problem of identifying the last thoracic and first lumbar vertebrae in CT images. The main purpose of this study is to improve the accuracy of labeling vertebrae of an automatic spine labeling system especially when the field of view is limited in the lower spine region. We present a dictionary-based classification method using a cascade of simultaneous orthogonal matching pursuit classifiers on 2D vertebral regions extracted from the maximum intensity projection images. The performance of the proposed method in terms of accuracy and speed has been validated by experimental results on hundreds of CT images collected from various clinical sites.

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