To address the issue of internal error measurement for rotary components， this paper exploited the advantages of industrial CT， which is non-destructive and capable of detecting the internal structure， and achieved the internal error measurement of rotary components through industrial CT images. Firstly， the axis parameters of the rotary body were calculated， and these parameters and Rodrigues’s formula were incorporated into the region growing method to enhance the accuracy of 3D point cloud segmentation. Subsequently， the objective function of the geometric model was established， and initial parameters of the geometric model were obtained through least squares linear fitting. Further， the optimal parameters of the geometric model were solved using Levenberg-Marquardt nonlinear fitting. Finally， the geometric model was utilized to compute the shape and position errors of the parts. Experimental results demonstrate the superiority of the proposed point cloud segmentation method in terms of accuracy when compared to other methods. Additionally， the surface reconstruction method exhibits better stability than the RANSAC algorithm. Compared with commercial VG software， the accuracy of shape and position error calculation is higher， with an increase of 8.5% in cylindricity， 21.1% in coaxiality， and 4.1% in verticality. Moreover， the cone tolerance calculation yields results within the design specifications， which satisfying the requirements of error measurement engineering.