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