Improved Snake Model Using Subsection Functions and Helmholtz Decomposition

Abstract

The snake model in segmentation of real-world images has some problems because the object in the image has complex textures and weak edges and the snake model has a drawback between noise smoothing and weak edge protection. To solve this problem, we adopt the snake model based on generalized gradient vector flow (GGVF) which has higher segmentation accuracy. We also adopt a new type of coefficient setting in the form of subsection function to improve the ability of protecting weak edges while smoothing noises. Moreover, we optimize the method by using Helmholtz decomposition. Experimental results and comparisons against other methods indicate that our proposed snake model demonstrates higher ability than that of other snake models in terms of real-world image segmentation.