Contextual Hopfield Neural Networks for Medical Image Edge Detection

Outlining of boundaries of organs and tumors in CT and MRI images are prerequisite in medical applications. In this paper, a single layer Hopfield neural network called Contextual Hopfield Neural Network (CHNN) is presented for finding the edges of CT and MRI images. Different from the conventional 2-D Hopfield neural networks, the CHNN maps the two-dimensional Hopfield network at the original image plane. With the direct mapping, the network is capable of incorporating pixels’ contextual information into a pixels’ 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, and more perceptual edge points than Laplacian-based, Marr-Hildreth’s, Canny’s, wavelet-based, and CHEFNN methods in noisy images.