We introduce robust variational segmentation techniques that are driven by an Edgeflow vector field. Variational image segmentation has been widely used during the past ten years. While there is a rich theory of these techniques in the literature, a detailed performance analysis on real natural images is needed to compare the various methods proposed. In this context, this paper makes the following contributions: (a) designing curve evolution and anisotropic diffusion methods that use Edgeflow vector fields to obtain good quality segmentation results over a large and diverse class of images, and (b) a detailed experimental evaluation of these segmentation methods. Our experiments show that Edgeflowbased anisotropic diffusion outperforms other competing methods by a significant margin.