Abstract
We present a novel method for the automatic segmentation of the vertebral bodies from 2D sagittal magnetic resonance (MR) images of the spine. First, a new affinity matrix is constructed by incorporating neighboring information, which local intensity is considered to depict the image and overcome the noise effectively. Second, the Gaussian kernel function is to weight chi-square distance based on the neighboring information, which the vital spatial structure of the image is introduced to improve the accuracy of the segmentation task. Third, an adaptive local scaling parameter is utilized to facilitate the image segmentation and avoid the optimal configuration of controlling parameter manually. The encouraging results on the spinal MR images demonstrate the advantage of the proposed method over other methods in terms of both efficiency and robustness.
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Acknowledgments
The authors gratefully acknowledge the useful comments and suggestions of the editors and reviewers, which have improved this manuscript. This work is partially supported by the National Science Foundation of China (project no. 31000450, no. 81000613) and the Major State Basic Research Development Program of China (973 Program, no. 2010CB732500).
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Zheng, Q., Lu, Z., Feng, Q. et al. Adaptive Segmentation of Vertebral Bodies from Sagittal MR Images Based on Local Spatial Information and Gaussian Weighted Chi-Square Distance. J Digit Imaging 26, 578–593 (2013). https://doi.org/10.1007/s10278-012-9552-9
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DOI: https://doi.org/10.1007/s10278-012-9552-9