In the application of image segmentation based on Shi's fast level set algorithm, there exist difficulties of setting thresholds,so a new approach is presented． In this new approach, the process of curve evolution can be seen as the pattern classification for the points of the curve constantly, so that the external velocity function for controlling curve evolution is redesigned,Both of the Bayesian classification rule and the Minimal distance classification rule are introduced by this new algorithm to work alternatively, in order to obtain the driving force of the external velocity acquired from image data indirectly, and therefore, the driving force does not come from thresholds anymore which are used for partitioning the image data, and the invalidation conditions for both of the classification rules are set as the iteration stop conditions in our new algorithm. Simulation experiments show that the new partition algorithm is not only more robust,which could evolve automatically by itself being adaptive to the image intensity information, but also has stronger anti-noise capability under the effect of noise; in the aspect of speed, it also executes much faster than several existing level set algorithms.
