提出了基于分数阶积分去噪的工业计算机断层切片图像中三维边缘曲面追踪算法.由于二维分数阶积分在去噪的同时能够很好地保持二维图像细节信息，且计算量较小，采用离散模板滤波形式去噪，效果良好.因此本文将二维分数阶积分连续理论推广至三维，利用二维分数阶积分傅里叶变换的可分离性，推导出三维离散去噪滤波模板，加入至三维边缘曲面追踪算法，克服了传统算法中Lalacian算子对噪音比较敏感的缺点.由于三维分数阶积分具有良好的去噪能力，可有效抑制噪音.实验结果表明，本文提出的基于三维分数阶积分去噪的算法能够从噪音污染工业计算机断层图像切片图像中追踪出高准确度的边缘曲面，有效改进了边缘曲面追踪算法易受噪音干扰影响的缺陷，通过体数据峰值信噪比和视觉效果比较，本文提出的算法性能优于基于三维高斯去噪的三维图像边缘曲面追踪算法.

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.