Topological region defect segmentation algorithm for cone-beam computed tomography slice images

According to the particularities of Cone-Beam Computed Tomography (CBCT) slice images, such as much noise information, low contrast, and variational region gray distribution, a defect segmentation algorithm is proposed, in which the topological regions of the whole image are segmented before segmenting the defects in each topological regions. Firstly, the gray topological structures in the slice image are obtained by image denoising pretreatment with mixture filter, iterative multi-threshold segmentation for the whole image, and extracting topological connective regions from the segmentation result. Then, region growing algorithm is applied into the defect detection in each topological structure. By this way, the low contrast segmentation in the whole image is translated into the higher contrast segmentation in the local topological structures to improving the precision and accuracy of the defect segmentation. Finally, the all extracted defects are identified by removing the "false defects" to obtain the real defects. This algorithm is used to processing the CBCT slice image of wax model of hollow turbine blade. The experiment result shows that the defects in the slice image are extracted effectively. The algorithm can be applied to the image segmentation processing which has similar characteristics with CBCT slice images.