Aiming at the deficiency of the traditional C-V model for image segmentation in terms of efficiency and accuracy of segmentation, this paper presents an improved C-V image segmentation model. Firstly, the level set function is restricted as a signed distance function by adding the internal energy term in the model, which could avoid the re-initialization and improve the efficiency of image segmentation. Secondly, the new regularization function of Heaviside function is chosen to improve the approximation effect and the accuracy of image segmentation. Finally, the regularization function is applied to replace the traditional Dirac function in C-V model with positive real functions. On the one hand, it's able to eliminate the latter inhibition of homogeneous areas near the border to detect non-initial active contour lines, and then makes the better global optimization features to improve the accuracy of image segmentation; on the other hand, it gives more simple model and improves the efficiency of image segmentation. Compared with the original C-V model, the numerical experiments show that the improved model has better efficiency and higher accuracy.