A Modified Image Segmentation Method Using Active Contour Model
Shiping Zhu, Ruidong Gao · Advances in computer science research · 2015
Active contours, or snakes, have extensive applications in image segmentation.Conventional snakes have several drawbacks, such as the initialization contour sensitivity and border leakage phenomenon.Many new methods have been proposed to address these problems.In this paper, we present an improved image segmentation method based on snakes.Firstly, we adopt the multi-step direction method to enlarge the scope of initial contour and obtain more precise edge map.Then, we decompose the Laplace operator to tangential direction and normal direction, weakening the border smoothing effect.Finally, two correlational self-adaptive weight functions are added to the two directions.Thus, the snakes can adaptively adjust the weights of smoothing item and diffusion item through the local image characteristics.Based on the subjective and objective evaluations, the proposed method outperforms the state-of-the-art methods and improves the segmentation accuracy.