Automatic lumbar vertebral identification using surface-based registration
Computers and Biomedical Research
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Spine detection and labeling using a parts-based graphical model
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Detection of 3D spinal geometry using iterated marginal space learning
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Automated vertebra identification from x-ray images
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Computer Vision and Image Understanding
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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.