Fuzzy Hopfield neural network with fixed weight for medical image segmentation

Abstract

Image segmentation is a process for dividing a given image into meaningful regions with homogeneous properties. A new two step approach is proposed for medical image segmentation using a fuzzy Hopfield neural network based on both global and local gray-level infor- mation. The membership function simulated with neuron outputs is de- termined using a fuzzy set, and the synaptic connection weights be- tween the neurons are predetermined and fixed to improve the efficiency of the neural network. The proposed method needs initial cluster centers. The initial centers can be obtained from the global information about the distribution of the intensities in the image, or from prior knowledge of the intensity of the region of interest. It is shown by experiments that the proposed fuzzy Hopfield neural network approach is better than most previous approaches. We also show that the global information can be used by applying the hard c -means to estimate the initial cluster centers.