Novel adaptive fast level set evolution without re-initialization

Level set methods have been extensively used in image segmentation,while the traditional level set methods is numerically necessary to keep the evolving level set function close to a signed distance function by periodically reinitializing,re-initializing the level set function is obviously a disagreement between the theory of the level set method and its implementation.Recently,a new variational formulation which completely eliminates the need of the costly re-initialization procedure has proposed by Li C et.at.The main drawback of this model is due to the one direction propagation,i.e.the initial curve either shrinking or expanding towards the object boundaries.In this paper,a new model is proposed subject to binary image for active contours to detect object in a given image,based on techniques of distance preserving level set method.The proposed models is free from the initial condition,furthermore the curve converge to the object boundary precisely,more importantly the curve evolution only take one iteration.