A modified Hopfield neural network with fuzzy c-means technique for multispectral MR image segmentation

Presents a modified Hopfield neural network with fuzzy c-means technique for segmenting multispectral MR brain images. The proposed approach is a novel unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique, and is suitable for parallel implementation in the application of medical image segmentation. The fuzzy c-means clustering strategy is included in the Hopfield neural network so as to eliminate the need of the weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the "within-class scatter matrix" principle. From the experimental results, it is shown that a near optimal solution can be obtained using the proposed method.