A robust improved Chan-Vese model based on Gaussian regularizing level set

In this paper, a new robust improved Chan-Vese (ICV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, signed pressure force (SPF) function and level set method. Compared with the ICV model, the proposed method is more robust to the location of the initial contour. Similar to the ICV model, a Gaussian regularizing level set method (GRLSM) is used to reduce the computational cost. Experimental results on some synthetic and real images show that our model is efficiency. Moreover, comparisons with the ICV model show that our model is more robust to the location of the initial contour.