Automated Identification of Thoracolumbar Vertebrae Using Orthogonal Matching Pursuit

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.

[1]  Sebastian P. M. Dries,et al.  Spine Detection and Labeling Using a Parts-Based Graphical Model , 2007, IPMI.

[2]  Cristian Lorenz,et al.  Automated model-based vertebra detection, identification, and segmentation in CT images , 2009, Medical Image Anal..

[3]  F. Pernus,et al.  Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine , 2010, Physics in medicine and biology.

[4]  Zhigang Peng,et al.  Automated Vertebra Detection and Segmentation from the Whole Spine MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[5]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[6]  Ronald M. Summers,et al.  Automated spinal column extraction and partitioning , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[7]  Benoit M. Dawant,et al.  Automatic Lumbar Vertebral Identification Using Surface-Based Registration , 2001, J. Biomed. Informatics.

[8]  Dorin Comaniciu,et al.  Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning , 2010, MCV.

[9]  Shang-Hong Lai,et al.  Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI , 2009, IEEE Transactions on Medical Imaging.

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  G. Konin,et al.  Lumbosacral Transitional Vertebrae: Classification, Imaging Findings, and Clinical Relevance , 2010, American Journal of Neuroradiology.

[12]  Jason J. Corso,et al.  Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features , 2008, MICCAI.

[13]  Guoyan Zheng,et al.  Automated Vertebra Identification from X-Ray Images , 2010, ICIAR.

[14]  Jun Ma,et al.  Hierarchical Segmentation and Identification of Thoracic Vertebra Using Learning-Based Edge Detection and Coarse-to-Fine Deformable Model , 2010, MICCAI.

[15]  P P Smyth,et al.  Vertebral shape: automatic measurement with active shape models. , 1999, Radiology.