A comparison of Hopfield neural network and Boltzmann machine in segmenting MR images of the brain

The segmentation of the images obtained from magnetic resonance imaging is an important step in the visualization of soft tissues in the human body. In this preliminary study, we report an application of the Hopfield neural network for the multispectral unsupervised classification of head magnetic resonance images. We formulate the classification problem as a minimization of an energy function constructed with two terms, the cost-term which is the sum of the squares errors, 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. We present here the segmentation result with two and three channels data obtained using the Hopfield neural network approach. We compare these results to those corresponding to the same data obtained with the Boltzmann machine approach.