Computed tomography (CT) images are a crucial resource for assessing the severity and prognosis of bone injuries caused by trauma or accident. Fracture detection in long bones is a very challenging task due to the limited resolution of the original CT images, as well as the complexity of bone structures and their possible fractures. Moreover, CT images are also susceptible to noise, partial volume effects, and intensity inhomogeneities. These make fracture detection more challenging. Sometimes, accurate fractured part segmentation and labeling require the participation of an expert. Due to these aspects, the orthopedic field is focusing on developing automated computer-aided diagnosis (CAD) systems for bone fracture detection and analysis. In this chapter, we have developed a CAD system that not only precisely extracts and assigns unique labels to each fractured piece by considering patient-specific bone anatomy, but also effectively removes the unwanted artifacts (like flesh) surrounded by bone tissues. In addition to this, the system will provide several fracture features, such as number of bones and number of fracture pieces per bone to analyze the severity and to decide on the optimal recovery plan/process. In the proposed system, experiments are conducted on real patient-specific CT images and have shown 95% accuracy. The result of the proposed system is compared both with several state-of-the-art segmentation methods, and with the clinical ground truth (i.e. annotated CT images) collected from experts.