REGION SIMILARITY RELATIONSHIP BETWEEN WATERSHED AND PENALIZED FUZZY HOPFIELD NEURAL NETWORK ALGORITHMS FOR BRAIN IMAGE SEGMENTATION

Abstract

A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.