Fracture detection in traumatic pelvic CT images

Authors:
Jie Wu;Pavani Davuluri;Kevin R. Ward;Charles Cockrell;Rosalyn Hobson;Kayvan Najarian
Affiliations:
Department of Computer Science, Virginia Commonwealth University, Richmond, VA;Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA;Department of Emergency Medicine, Virginia Commonwealth University, Richmond, VA;Department of Emergency Medicine, Virginia Commonwealth University, Richmond, VA and VCURES;Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA and VCURES;Department of Computer Science, Virginia Commonwealth University, Richmond, VA and Department of Radiology
Venue:
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
Year:
2012

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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.