Shape Prior Based Hybrid Active Contour Model and its Applications in Image Segmentation
Cao Dong-me · 2014
In this paper,a new Shape-prior based Hybrid Active Contour(SHAC)model was presented for segmentation.By using level set method,this model combines boundary and adaptive region information together and learns an optimal prior shape from the training set.It takes the boundary and adaptive region feature as local information while prior shape as global information.The model combines global and local information in the process of iteration to guide the evolution of deformative curve and achieve the goal of segmenting target objects.Experiments show that compared with GAC,C-V,and RSF models,SHAC model displays its advantages not only in the segmentation of image strong noise and weak boundary,but also in the image with low contrast resolution,complicated background and contributes improved accuracy.