VFCCV snake: A novel active contour model combining edge and regional information

Abstract

Active contour models have been widely used for image segmentation. Among leading models of active contour is vector-field convolution (VFC), a parametric active contour that improves the popular gradient vector flow (GVF) model. However VFC is still sensitive to noise and can be easily trapped in cluttered regions of an image because it only considers edge information. Based on the geometric active contour model proposed by Chan and Vese, this paper introduces a novel active contour model that incorporates region information in VFC in order to take advantage of edge and regional information. This new model, which we refer to as VFCCV snake, is implemented in the parametric active contour framework, and has control on topology especially in noisy images and images with boundary gaps. Experimental results on both synthetic and real images show superior performance of our VFCCV snake to state-of-the-art leading active contour methods.