Neural network based segmentation using a priori image models

We examine image segmentation using a Hopfield neural network. Image segmentation is posed as an optimization problem, and is correlated with the energy function of the neural network. By carefully designing the optimization criterion for segmentation, it is possible to identify the bias inputs and the interconnection weights of the corresponding neural network. We provide a general framework for the design of the optimization criterion, which consists of a component based on the observed image, and another component based on an a priori image model. As an application, we consider a smoothness constraint for the segmented image as our a priori information, and solve a gray-level based segmentation problem. The feasibility of using the neural network architecture based on this optimization criterion for the segmentation of masses in mammograms is demonstrated.