In this paper, we propose a new paradigm to carry out the registration task with a dense deformation field derived from the optical flow model and the active contour method. The proposed framework merges different tasks such as segmentation, regularization, incorporation of prior knowledge and registration into a single framework. The active contour model is at the core of our framework even if it is used in a different way than the standard approaches. Indeed, active contours are a well-known technique for image segmentation. This technique consists in finding the curve which minimizes an energy functional designed to be minimal when the curve has reached the object contours. That way, we get accurate and smooth segmentation results. So far, the active contour model has been used to segment objects lying in images from boundary-based, region-based or shape-based information. Our registration technique will profit of all these families of active contours to determine a dense deformation field defined on the whole image. A well-suited application of our model is the atlas registration in medical imaging which consists in automatically delineating anatomical structures. We present results on 2D synthetic images to show the performances of our non rigid deformation field based on a natural registration term. We also present registration results on real 3D medical data with a large space occupying tumor substantially deforming surrounding structures, which constitutes a high challenging problem.