The active contour model based on the local Gaussian fitting utilizes the average and variance information to fit the image information. Compared with the traditional active contour models which only utilize the gray average information, this model can segment the complex medical image successfully. However, this model only utilizes the local information of image to model, so the convergence speed is slow. In addition, the traditional Heaviside function is utilized to establish the energy function, which leads to the limited segmentation accuracy. Aimed at these defects, the global Gaussian fitting term is introduced to improve the Heaviside function. Using the method of adaptive adjustment, an active contour segmentation model based on the local and global Gaussian fitting is obtained. The improved model can not only segment the images with same average but different variance, but also segment the inferior medical images effectively, and the performance of the improved model is verified by experiments.