Hopfield neural network for the multichannel segmentation of magnetic resonance cerebral images

摘要

In this paper, 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 minima and be closer to the global minimum. Also, to ensure the convergence of the network and its utility in the 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 remain there ever after. We present here, segmentation results of two patients data diagnosed with a metastatic tumor and multiples sclerosis in the brain.