A New Algorithm for Image Segmentation Base On CV Model

The active contour model without edges (CV model) is one of the most successful variational models in image segmentation. The paper proposes a new method to improve the efficiency of CV model. Firstly, the energy function of CV model is substituted by the form of total variation which has the same stable solution with the original model. Secondly, the dual method is used to solve the minimal value of the total variation formulation. At the same time, a speed term is introduced to improve the convergence speed. The speed term can reduce the reliance on gradient and improve the robust. The new method can get different segmentation results from same image by changing speed term. The first experiment solves the partial differential equations by the gradient descent method and the new method respectively, it shows that the proposed method is not only faster than gradient descent method , but also robust to noise with more integrated segmentation result and more smooth edges. The second experiment solve the partial differential equations by the new method but the speed term is different, it shows that the convergence speed is lower and the segmentation result has more homogeneity regions when the speed term is small. The experiments show that the propose method is effective and potent.