Most of the existing level set methods for multi-region use complex energy functions to drive the evolution of multiple level sets

which not only makes the model more complex but also has many limitations. In this paper

we propose a fast multi-region active contour method based on texture features by using an arbitrary number of level sets functions to segment an image into regions of the corresponding amount. We first establish a joint distribution of color and texture information and bring it into data term of energy function. Then

we introduce the smooth probability label and establish an update equation of level set function for multi-region driven by probability labels. And

each level set for different regions is projected into the discrete space to get a series of approximate labels. Due to these labels

a prior probability based on multi-level-sets is obtained

which introduce the evolution information of multiple contours into the statistical framework. The statistical parameters of each region are also updated iteratively from the smooth probability labels by minimizing the energy function. We experimentally compare the proposed approach with other methods on complicated real-world images and demonstrate its good performance in practice.