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Proceedings Paper

Computerized scheme for vertebra detection in CT scout image
Author(s): Wei Guo; Qiang Chen; Hanxun Zhou; Guodong Zhang; Lin Cong; Qiang Li
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Paper Abstract

Our purposes are to develop a vertebra detection scheme for automated scan planning, which would assist radiological technologists in their routine work for the imaging of vertebrae. Because the orientations of vertebrae were various, and the Haar-like features were only employed to represent the subject on the vertical, horizontal, or diagonal directions, we rotated the CT scout image seven times to make the vertebrae roughly horizontal in least one of the rotated images. Then, we employed Adaboost learning algorithm to construct a strong classifier for the vertebra detection by use of Haar-like features, and combined the detection results with the overlapping region according to the number of times they were detected. Finally, most of the false positives were removed by use of the contextual relationship between them. The detection scheme was evaluated on a database with 76 CT scout image. Our detection scheme reported 1.65 false positives per image at a sensitivity of 94.3% for initial detection of vertebral candidates, and then the performance of detection was improved to 0.95 false positives per image at a sensitivity of 98.6% for the further steps of false positive reduction. The proposed scheme achieved a high performance for the detection of vertebrae with different orientations.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853Q (24 March 2016); doi: 10.1117/12.2216744
Show Author Affiliations
Wei Guo, Shenyang Aerospace Univ. (China)
Qiang Chen, Shenyang Aerospace Univ. (China)
Hanxun Zhou, Liaoning Univ. (China)
Guodong Zhang, Shenyang Aerospace Univ. (China)
Lin Cong, Shanghai United Imaging Healthcare Co., Ltd. (China)
Qiang Li, Shanghai United Imaging Healthcare Co., Ltd. (China)
Shanghai Advanced Research Institute (China)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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