A comprehensive method of contour extraction for industrial computed tomography images

A novel contour extraction method is presented in this paper. In reverse engineering based on industrial computed tomography, quality of extracted contours greatly influences the reverse results. As a widely used method of edge extraction, the wavelet transform modulus maximum algorithm is also employed to extract contours. It often gets results through thresholding wavelet transform coefficients and then follows it up by edge tracking. Generally, threshold affects the accuracy of edge localization, and additional edge tracking increases complexity. To overcome these shortcomings, in this paper, we present a method of contour extraction synchronizing with edge tracking but omitting the thresholding process. Firstly, wavelet transform is performed on an original gray image to get modulus matrix. Secondly, by manually clicking inside and outside of the desired contour on the original image, a starting contour point can be obtained through determination of local modulus maximum value. Thirdly, on the mask centered on the starting contour point, a next contour point can be automatically detected by searching local modulus maximum value. Then, starting from that new contour point and recursively seeking the local modulus maximum value, an occlusive contour can be correctly found. Experiments show that this method can directly extract continuous and single-pixel wide contours from gray image.

► We merge edge extraction and edge tracking into one step. ► Continuous and single-pixel wide contours can be gained. ► This method is performed on gray image and needs no binarization. ► This method needs no threshold, preprocessing and post-processing. ► A little human–machine interaction improves the flexibility.