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27 March 2009 A machine learning approach to extract spinal column centerline from three-dimensional CT data
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594T (2009) https://doi.org/10.1117/12.810982
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The spinal column is one of the most important anatomical structures in the human body and its centerline, that is, the centerline of vertebral bodies, is a very important feature used by many applications in medical image processing. In the past, some approaches have been proposed to extract the centerline of spinal column by using edge or region information of vertebral bodies. However, those approaches may suffer from difficulties in edge detection or region segmentation of vertebral bodies when there exist vertebral diseases such as osteoporosis, compression fracture. In this paper, we propose a novel approach based on machine learning to robustly extract the centerline of the spinal column from threedimensional CT data. Our approach first applies a machine learning algorithm, called AdaBoost, to detect vertebral cord regions, which have a S-shape similar to and close to, but can be detected more stably than, the spinal column. Then a centerline of detected vertebral cord regions is obtained by fitting a spline curve to their central points, using the associated AdaBoost scores as weights. Finally, the obtained centerline of vertebral cord is linearly deformed and translated in the sagittal direction to fit the top and bottom boundaries of the vertebral bodies and then a centerline of the spinal column is obtained. Experimental results on a large CT data set show the effectiveness of our approach.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Caihua Wang, Yuanzhong Li, Wataru Ito, Kazuo Shimura, and Katsumi Abe "A machine learning approach to extract spinal column centerline from three-dimensional CT data", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594T (27 March 2009); https://doi.org/10.1117/12.810982
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