The Chan-Vese model for image segmentation involves many parameters, which needs to be tuned artificially for images from different modalities. The work is tedious, laborious and time-consuming. To overcome this problem, an adaptive regularization active contour model was proposed. Firstly, the data term of Chan-Vese model was reduced. Secondly, the length term was substituted by the improved edge weighted H ¹ regularization term. Finally, a new active contour model was proposed without any parameters. In the segmentation experiments, the proposed model was less sensitive to the size and location of initial contour with strong noise resistance, and the average segmentation time of 6 images was 1.5834 s while the number of iterations was 19. The experimental results show that, the proposed model can handle images with intensity inhomogeneity and strong noise well without manual adjustment of parameters, and the segmentation speed is faster compared with other active contour models.