The internal complex structure precision measurement of key part is a challenge in the field of high quality manufacturing. When the industrial CT technology is used to achieve precise measurement of the internal structure of the object, it faces problems of grayscale inhomogeneity, blurred edges, and artifacts of the target image. In view of these, the local energy minimization model (RSF) image segmentation method is investigated in this article. The natural gradient and AdamW algorithms are used to improve the convergence speed and parameter adaptivity of the RSF model, respectively. First, the approximate natural gradients are computed on the statistical manifold to improve gradient descent efficiency and RSF model convergence speed. Secondly, the AdamW algorithm is utilized to realize the adaptive control of the scale of the Gaussian kernel function of the RSF model. Compared with the classical RSF model, the improved RSF model reduces the number of iterations by 1 353, the number of iterations by about 76. 79% , the iteration time by about 43. 61% , and the low measurement errors of the probe-radius and the diameter of jet fuel nozzle cylinder, which not only maintains the sub-pixel segmentation accuracy of the original model, but also significantly improves the convergence speed and robustness of the model.