Technical Program

Paper Detail

Paper:	FR-PM-PS1.2
Session:	Atlas and Model Based Image Segmentation
Time:	Friday, April 7, 13:30 - 14:50
Presentation:	Poster
Title:	Automated Spinal Column Extraction and Partitioning
Authors:	Jianhua Yao; National Institutes of Health
	Stacy O'Connor; National Institutes of Health
	Ronald M. Summers; National Institutes of Health
Abstract:	This paper presents an approach to automatically segment and partition the spinal column from routine 5 mm chest and/or abdominal CT images. The segmented spinal column has great value in image registration, content based image retrieval, spine deformity analysis, and organ localization. In our method, first a simple thresholding is employed to obtain the initial spine segmentation. Then a hybrid method based on the watershed algorithm and directed graph search is applied to extract the spinal canal. After that, a four-part vertebra model (vertebral body, spinous process, and left/right transverse processes) is fitted to segment the vertebral region and separate it from adjacent ribs and other structures. Curved reformations in sagittal and coronal directions are generated and aggregated intensity profiles along the spinal cord are analyzed to partition the spinal column into vertebrae. The algorithm has been tested on 71 CT scans. Results showed that our algorithm successfully extracted and partitioned 69 spinal columns, with only 2 cases that had one missed partition at the T1-T2 level.

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