Novel Convex Active Contour Model Using Local and Global Information

In this paper, we propose a novel region-based active contour model for image segmentation. Our model incorporates the global and local information in the energy function, enabling efficient segmentation of images while accounting for intensity in homogeneity. Another interesting property of the proposed model is its convexity, making it independent of the initial condition and hence ideal for an automatic segmentation. Furthermore, the energy function of the proposed model is minimized in a computationally efficient way by using the Chambolle method. Experimental results on natural and medical images demonstrate the performance of our model over the current state-of-the-art.