Cone-beam Computed Tomography Image Pretreatment and Segmentation

Segmentation accuracy is critical in CBCT (cone-beam computed tomography) nondestructive detection. And it is influenced by the segmentation accuracy of CBCT serial slice images. However, the noise and artifacts in CBCT images make it hard to segment CBCT images precisely. To increase CBCT image segmentation accuracy, the 3D information in CBCT images should be fully used. We proposed and compared four connection models for CBCT images pretreatment. They can decrease the noise in CBCT images. Moreover, we propose a 3D CBCT image segmentation method based on the accumulated FCM_S. In the experiment, CBCT slice images of a workpiece are segmented by our proposed method and comparing methods. The segmentation results certified the effectiveness of our method.