We propose a novel and fast way to perform 2D-3D registration between available intra-operative 2D images with pre-operative 3D images in order to provide better image-guidance. The current work is a feature based registration algorithm that allows the similarity to be evaluated in a very efficient and faster manner than that of intensity based approaches. The current approach is focused on solving the problem for neuro-interventional applications and therefore we use blood vessels, and specifically their centerlines as the features for registration. The blood vessels are segmented from the 3D datasets and their centerline is extracted using a sequential topological thinning algorithm. Segmentation of the 3D datasets is straightforward because of the injection of contrast agents. For the 2D image, segmentation of the blood vessel is performed by subtracting the image with no contrast (native) from the one with a contrast injection (fill). Following this we compute a modified version of the 2D distance transform. The modified distance transform is computed such that distance is zero on the centerline and increases as we move away from the centerline. This allows us a smooth metric that is minimal at the centerline and large as we move away from the vessel. This is a one time computation, and need not be reevaluated during the iterations. Also we simply sum over all the points rather than evaluating distances over all point pairs as would be done for similar Iterative Closest Point (ICP) based approaches. We estimate the three rotational and three translational parameters by minimizing this cost over all points in the 3D centerline. The speed improvement allows us to perform the registration in under a second on current workstations and therefore provides interactive registration for the interventionalist.