Image segmentation based on geometric active contour model

A geometric active contour model is proposed to reduce the influence of noise on image segmentation. The corresponding partial differential equations evolved by the level set curve are got through variational principle. Prior information of the regions and boundaries of the image is considered in this model and the statistical information of the image is considered as well. Moreover, a penalized term is used as an internal energy term to avoid the time-consuming re-initialization process. To verify the efficiency of the proposed model, a segmentation instance based on simple Gauss probability density function is given, and the additive operator splitting (AOS) scheme which is efficient and unconditionally stable is employed. Experimental results show that the proposed model has high accuracy, efficiency and noisy resistance.