Abstract
In this article, we present an approach for the segmentation of magnetic resonance images of the brain, based on Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term, that is a sum of errors' squares, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be more close to the global minimum. Also, to ensure the convergence of the network and its utility in clinic with useful results, the minimization is achieved in a way that after a prespecified period of time, the energy function can reach a local minimum close to the global minimum and remains there ever after. We present here, segmentation results of a subject data diagnosed with a metastatic tumor in the brain.