Cellular neural network architecture for Gibbs random field-based image segmentation

We describe in this paper a novel cellular connectionist neural network model for the implementation of clustering-based Bayesian image segmentation with Gibbs random field spatial constraints. The success of such an algorithm is largely due to the neighborhood constraints modeled by the Gibbs random field. However, the iterative enforcement of the neighborhood constraints involved in the Bayesian estimation would generally need tremendous computational power. Such computational requirement hinders the real-time application of the Bayesian image segmentation algorithms. The cellular connectionist model proposed in this paper aims at implementing the Bayesian image segmentation with real-time processing potentials. With a cellular neural network architecture mapped onto the image spatial domain, the powerful Gibbs spatial constraints are realized through the interactions among neurons connected through their spatial cellular layout. This network model is structurally similar to the conventional cellular network. However, in this new cellular model, the processing elements designed within the connectionist network are functionally more versatile in order to meet the challenging needs of Bayesian image segmentation based on Gibbs random field. We prove that this cellular neural network does converge to the desired steady state with a properly designed update scheme. An example of CT volumetric medical image segmentation is presented to demonstrate the potential of this cellular neural network for a specific image segmentation application.