Abstract: Level set methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. A new variational level set evolving algorithm without re-initialization is presented in this paper. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected. Keywords: Level Set Methods, Evolving Algorithm, without Re-initialization, Image Segmentation 1. Introduction Level set methods were first introduced by Osher and Sethian [1] to capture moving fronts. Active contours were introduced in order to segment objects in images using dynamic curves. Level set methods provide mathematical and computational tools for the tracking of evolving interfaces with sharp corners and cusps, and topological changes. They efficiently compute optimal robot paths around obstacles, and extract clinically useful features from the noisy output of images. In traditional level set methods, re-initialization, a technique for periodically re-initializing the level set function to a signed distance function, has been used as a numerical algorithm for maintaining stable curve evolution. However, many proposed re-initialization schemes have the undesirable side effect of moving the zero level set away from its original location. As such, there are certain drawbacks associated with re-initialization [2]. In this paper, we present a new variational level set evolving algorithm without re-initialization [3]. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature [4]. This algorithm can be computed more efficiently and implemented using only a very simple finite difference scheme. Meanwhile, the initial contour can be anywhere in the image and a larger time step can be used to speed up the evolution.