Traditional snakes suffer from slow convergence speed (many control points) and difficult to adjust weighting factors for internal energy terms. We propose an alternative formulation using cubic B-splines, where the knot spacing is variable and controlled by the user. A larger knot spacing allows us to reduce the number of parameters, which increases optimization speeds. It also eliminates the need for internal energies, which improves user interactivity. The optimization procedure is embedded into a multi-resolution image representation, where the number of snake points is adapted to the image grid spacing by correctly adjusting the spline knot spacing. Hence, the proposed method provides a multi-scale approach in both the image and parametric contour domain. Our technique provides fast optimization of the initial snake curve and leads to more stable algorithms in noisy imaging environments. Several biomedical examples of applications are included to illustrate the versatility of the method.