Abstract: As a fundamental aspect of bone fracture treatment, fracture reduction plays a decisive role in restoring the structural integrity and function of bones. At present, fracture reduction techniques mostly rely on semi-automatic interaction methods or healthy-side bone templates for registration, which have many limitations in clinical practice. In order to enhance treatment efficiency and accuracy, an automatic fracture reduction algorithm is proposed. This algorithm utilizes the similarity of fracture cross-sections for registration, thereby reducing the workload of physicians and eliminating the need for a healthy-side bone template. Initially, the closed edge is identified and extracted by analyzing the differences in the fracture surface and the calorific value diagram of the roughness distribution. Next, the fracture section is determined by using the identified closed edge as a guideline for regional expansion and similarity matching. During the registration phase, the iterative closest point (ICP) algorithm is highly sensitive to distance. Therefore, the geometric features of point clouds are incorporated into the objective function of the registration algorithm to mitigate the influence of noise, and fracture section registration is implemented one by one. Finally, the algorithm is tested and compared on 180 simulated datasets and 16 publicly available datasets. The results show that the proposed algorithm significantly improves the registration accuracy, and the registration error of clinical bone fracture cases is controlled within 1.7 mm.