A hybrid active contour model with structure feature for image segmentation

Qi Ge, Liang Xiao, Liqian Wang, Zhengrong Zhang, Zhihui Wei

Abstract: We propose a structured feature active contour model based on the level set method for image segmentation. We make the following two contributions. First, an adaptive data fitting term detects intensity variation based on the direction and global information. Second, integrated with a structured gradient vector flow (SGVF) method, we formulate a new regularization term with respect to the level set function via the duality formulation to penalize the length of active contour. We compare the proposed method to the classical active contour methods and demonstrate through the experiments on synthetic and medical images.