Knowledge of pulmonary fissure anatomy plays an important role in localization of lesions and evaluation of lung disease. In computed tomography images, pulmonary fissure detection is an intricate task due to factors such as pathological deformation, partial volume effect and noise. To solve the problem, a novel method based on shape features is proposed for pulmonary fissure detection. Firstly, the orientation information and magnitude information of pulmonary fissures are fused to enhance pulmonary fissures and suppress interferences. Then region property analysis algorithm is used to remove interferences like airways and vessels for pulmonary fissure identification. Finally, surface curvature approach is utilized to remove adhering interferences for pulmonary fissure segmentation. The performance of the proposed method is validated in experiments with a publicly available LOLA11 dataset. Compared with manual references, the proposed method acquired a high median F1-score of 0.8451. Experimental results show that the proposed method has a good performance in pulmonary fissure segmentation.