Abstract
A reliable detection and definitive labeling of vertebrae can be difficult due to factors such as the limited imaging coverage and various vertebral anomalies. In this paper, we investigate the problem of identifying the last thoracic vertebra and the first lumbar vertebra in CT images, aiming to improve the accuracy 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 (SOMP) classifiers on 2D vertebral regions extracted from the maximum intensity projection (MIP) 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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Wu, T., Jian, B., Zhou, X.S. (2011). Automated Identification of Thoracolumbar Vertebrae Using Orthogonal Matching Pursuit. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_16
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DOI: https://doi.org/10.1007/978-3-642-24319-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
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