A novel snake model without re-initialization for image segmentation

In this paper, we present a new variational formulation of geometric snake for image segmentation. Our formulation includes an internal energy term that penalizes the deviation of the level set function from a signed distance function and stopping term related to a particular segmentation of the image instead of gradient. They force the level set function to be close to a signed distance function, therefore completely eliminate the need of the costly re-initialization procedure. Significantly larger time step can be used for solving the evolution equation to speed up the evolution. The level set formulation is easily implemented by simple finite difference scheme that is computationally more efficient. Meanwhile not only the initial curve can be anywhere in the image, but also interior contours can be automatically detected. Experiment results on image segmentation show that our algorithm has very good performance.