12 March 2010 Automated extraction method for the center line of spinal canal and its application to the spinal curvature quantification in torso X-ray CT images
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Abstract
X-ray CT images have been widely used in clinical routine in recent years. CT images scanned by a modern CT scanner can show the details of various organs and tissues. This means various organs and tissues can be simultaneously interpreted on CT images. However, CT image interpretation requires a lot of time and energy. Therefore, support for interpreting CT images based on image-processing techniques is expected. The interpretation of the spinal curvature is important for clinicians because spinal curvature is associated with various spinal disorders. We propose a quantification scheme of the spinal curvature based on the center line of spinal canal on CT images. The proposed scheme consists of four steps: (1) Automated extraction of the skeletal region based on CT number thresholding. (2) Automated extraction of the center line of spinal canal. (3) Generation of the median plane image of spine, which is reformatted based on the spinal canal. (4) Quantification of the spinal curvature. The proposed scheme was applied to 10 cases, and compared with the Cobb angle that is commonly used by clinicians. We found that a high-correlation (for the 95% confidence interval, lumbar lordosis: 0.81-0.99) between values obtained by the proposed (vector) method and Cobb angle. Also, the proposed method can provide the reproducible result (inter- and intra-observer variability: within 2°). These experimental results suggested a possibility that the proposed method was efficient for quantifying the spinal curvature on CT images.
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Tatsuro Hayashi, Xiangrong Zhou, Huayue Chen, Takeshi Hara, Kei Miyamoto, Tatsunori Kobayashi, Ryujiro Yokoyama, Masayuki Kanematsu, Hiroaki Hoshi, Hiroshi Fujita, "Automated extraction method for the center line of spinal canal and its application to the spinal curvature quantification in torso X-ray CT images", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233F (12 March 2010); doi: 10.1117/12.843956; https://doi.org/10.1117/12.843956
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