A robust active contour model driven by an order-statistic filtering and coherence-enhancing diffusion (OSFCED) filter for fast image segmentation is proposed. The main idea of the order-statistic filtering is to construct the edge force function (EFF) to quickly and adaptively attract evolving curves to target boundaries while a coherence-enhancing diffusion (CED) filter aims at filtering noise and enhancing target boundaries so that the segmentation efficiency can be improved. In addition, the computation of the EFF and CED function is completed before iterations. Therefore, the computational cost of the proposed model is low during curve evolution. Furthermore, the addition of optimized distance regularization term and optimized length term makes curve evolution smoother and more stable. Experiments performed show that the proposed model is robust to initial contour, parameter and has higher segmentation efficiency for images with intensity inhomogeneity.