The existing image segmentation models have problems of being sensitive to initialization information

slower segmentation and leaked weak image boundary regions.This paper presents a hybrid fast segmentation model which utilizes the local statistics of bias field approximated images

the global information of compatibility and the distance regularization method.Then the model is embedded into level set framework.In addition

a dual termination standard is constructed to improve the speed of segmentation.Experiments on synthetic and real images are conducted to verify the efficiency of our model.Moreover

comparisons with the well-known CV model

nonlinear adaptive level set model and region scalable fitting model demonstrate that the proposed model reduces the sensitivity to the initialization and improves the segmentation speed by 3~5 times.