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International Journal of Biomedical Imaging
Volume 2012, Article ID 327198, 10 pages
http://dx.doi.org/10.1155/2012/327198
Research Article

Fracture Detection in Traumatic Pelvic CT Images

1Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
2Department of Electrical and Computer Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
3Department of Emergency Medicine, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
4Virginia Commonwealth University Reanimation Engineering Science Center (VCURES), Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
5Department of Radiology, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA

Received 2 July 2011; Revised 30 September 2011; Accepted 30 September 2011

Academic Editor: Shan Zhao

Copyright © 2012 Jie Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.