Fast image segmentation using C-means based Fuzzy Hopfield neural network

In this paper, we propose a fast C-means based training of Fuzzy Hopfield neural network and apply it to image segmentation. According to the other ways which usually take a long time, we define a fast method for image segmentation. We present a new objective function, and its minimization by Lyapunov energy function which is based on two dimensional fuzzy Hopfield neural network. This objective function is the same energy function Hopfield neural network which is improved, and includes average distance between image pixels and cluster centers. In this new method, numbers of iterations are less than the other methods it means the proposed method has a faster convergence rate in comparison with the other ways. Therefore, Fuzzy Hopfield neural network method provides image segmentation better than the other methods according to experimental results.