Three dimensional edge surfaces tracked from noisy industrial CT slice images

A 3D edge surface denoised tracking algorithm is proposed to reconstruct high accuracy edge surfaces from the noisy industrial CT slices based on the 3D fractional-order integral. The 2D fractional-order integral method has effective denoising ability to preserves the texture detail of the image, and it has low computation complexity and easy implementation due to the filtering mask. In this paper, the 2D fractional-order integral has been extended to three-dimensional images, its 3D continuous theory and the discrete filtering masks are also proposed, we call it volumetric fractional-order integral. Since the Laplacian operator shows the sensitivity to the noise, the traditional 3D edge surface tacking method cannot extract the high precision 3D edge surface from noisy slice images effectively, the 3D fractional-order integral is added to the tracking method to overcome the existed shortcoming. Our method is able to detect and extract the 3D edge surface of sub-voxel accuracy from the 2D noisy industrial CT slice images. The experiments have reported very encouraging results according to signal noise ratio and visual effect by comparing it to the tacking method based on 3D Gaussian denosing method.