A survey of fracture detection techniques in bone X-ray images

Radiologists interprets X-ray samples by visually inspecting them to diagnose the presence of fractures in various bones. Interpretation of radiographs is a time-consuming and intense process involving manual examination of fractures. In addition, clinician’s shortage in medically under-resourced areas, unavailability of expert radiologists in busy clinical settings or fatigue caused due to demanding workloads could lead to false detection rate and poor recovery of the fractures. A comprehensive study is imparted here covering fracture diagnosis with the aim to assist investigators in developing models that automatically detects fracture in human bones. The paper is presented in five folds. Firstly, we discuss data preparation stage. Second, we present various image-processing techniques used for fracture detection. Third, we analyze conventional and deep learning based techniques for diagnosing bone fractures. Fourth, we make comparative analysis of existing techniques. Fifth, we discuss different issues and challenges faced by researches while dealing with fracture detection.