OPTIMIZATION NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC-RESONANCE IMAGES

Segmentation of the images obtained from magnetic resonance imaging (MRI) is an important step in the visualization of soft tissues in the human body. The multispectral nature of the MRI has been exploited in the past to obtain better performance in the segmentation process. The new emerging field of artificial neural networks promises to provide unique solutions for the pattern classification of medical images. In this preliminary study, we report the application of Hopfield neural network for the multispectral unsupervised classification of MR images. We have used winner-take-all neurons to obtain a crisp classification map using proton density-weighted and T2-weighted images in the head. The preliminary studies indicate that the number of iterations to reach "good" solutions was nearly constant with the number of clusters chosen for the problem.