In this paper, the special design of a Hopfield neural network, called contextual Hopfield neural network (CHNN), is presented for finding the edges of CT and MRI images. Different from conventional 2D Hopfield neural networks, the CHNN maps the 2D Hopfield network at the original image plane. With this direct mapping, the network is capable of incorporating pixel contextual information into a pixel's labeling procedure. As a result, the effect of tiny details or noises will be effectively removed by the CHNN and the drawback of disconnected fractions can be overcome. Furthermore, the problem of satisfying strong constraints can be alleviated and results in a fast converge. Our experimental results show that the CHNN can obtain more appropriate, more continued edge points than Laplacian-based, Marr-Hildreth's, Canny's, and wavelet-based methods
['Chuan-Yu Chang']
A contextual-based Hopfield neural network for medical image edge detection