Computerized scheme for vertebra detection in CT scout image

Wei Guo; Qiang Chen; Hanxun Zhou; Guodong Zhang; Lin Cong; Qiang Li

doi:10.1117/12.2216744

24 March 2016 Computerized scheme for vertebra detection in CT scout image

Wei Guo, Qiang Chen, Hanxun Zhou, Guodong Zhang, Lin Cong, Qiang Li

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97853Q (2016) https://doi.org/10.1117/12.2216744
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Wei Guo, Qiang Chen, Hanxun Zhou, Guodong Zhang, Lin Cong, and Qiang Li "Computerized scheme for vertebra detection in CT scout image", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853Q (24 March 2016); https://doi.org/10.1117/12.2216744

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