Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Heterogeneous computing for vertebra detection and segmentation in x-ray images
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
Machine Vision and Applications
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Automated identification of vertebra bodies from medical images is important for further image processing tasks. This paper presents a graphical model based solution for the vertebra identification from X-ray images. Compared with the existing graphical model based methods, the proposed method does not ask for a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on digitially reconstructed radiographs of twenty-one cadaver spine segments verified its performance.