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. 1. Introduction Pelvic fractures are high energy injuries that constitute a major cause of death in trauma patients. According to the Centers for Disease Control and Prevention (CDC), trauma injury kills more people between the ages of 1 and 44 than any other disease or illness. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, caused mainly by sports, falls, and motor vehicle accidents, contribute to a large number of mortalities every year [1, 2]. Traumatic pelvic injuries and associated complications, such as severe hemorrhage multiple organ dysfunction syndrome (MODS), result in the mortality rate from 8.6% to 50% . When combined with other injuries in the body, for instance, the abdomen, the chance of mortality is even higher . In general, a pelvic fracture can be associated hemorrhage, neurologic injury, vascular injury, and organ damage, as all of the vital structures run through pelvis. Pain and impaired mobility are normally the results of nerve and internal organ damage associated with the pelvic fracture [5–7]. Patient data, in particular, medical images such as computed tomography (CT) images, contain a significant amount of information, and it is crucial for physicians to make diagnostic decisions as well as treatment planning on the basis of this information and other patients’ data. Currently, a large portion of the data is not optimally and comprehensively utilized, because information held in the data is inaccessible through visual observation or simple traditional computational methods. Information contained in pelvic CT images is a very important resource for the assessment of the
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