Removal of Surface Artifacts of Material Volume Data with Defects

The three-dimensional defect distribution in material test specimens is a very important piece of information for us to understand the deformation and failure mechanism of materials. This distribution is sometimes complicated by the surface roughness of specimens in the defect detection of computed tomography data. In this paper, we proposed a new local differentiation algorithm to remove the surface artifacts caused by surface roughness in the defect detection of material specimens from computed tomography (CT) volume data. The accuracy of our method is compared with a traditional scan-line algorithm in terms of defect volume fraction measured in an independent scanning electron microscope (SEM) test. The experimental result indicates that our method is significantly better than the existing scan-line approach for predicting the defect volume fraction.