A Unified Method Based on Wavelet Transform and C-V Model for Crack Segmentation of 3D Industrial CT Images

Abstract

Accurate segmentation of cracked body from three-dimensional (3D) industrial Computed Tomography (CT) images is an important step in the process of crack measurement and automatic recognition. In this paper we present a fast method for the segmentation of cracked body. The improved algorithm incorporates wavelet transform and Chan and Vese (C-V) model as key components. The 3D wavelet transform is applied for detecting rough edges. Then region growing is used to find a suitable region which contains cracked body. Based on the resulting volume data, 3D C-V model is used to capture the edges of cracked body. The improved method can locate rough regions by using wavelet modulus maxima, which not only reduces the amount of data C-V model processed, but also provides initial contour surface that can accelerate the convergence speed of C-V model. Experimental results illustrate our method can accurately detect the cracked surface, as well as give computational savings of segmentation which satisfy the demand of defects detection of industrial CT.