Stochastic approach to texture analysis using probabilistic neural networks and Markov random fields

Images are statistical in nature due to random changes and noise,therefore it is sometimes an advantage to treat image functions as a realization of a stochastic process. An advantage of stochastic random field models is that they need only a few parameters to describe a region or texture. In this paper textural images are modeled as a realization of Markov Random Fields such as binomial and autoregressive Markov random fields. Parameters of each model are estimated and considered as features of the textural images. The extracted features are incorporated in either a probabilistic neural network (PNN) or a deterministic back propagation neural networks for the purpose of classification and differentiation between various textural images. The PNN and the learning algorithm are discussed in this paper in details. To train back propagation neural network, a hybrid training algorithm is proposed. This hybrid algorithm takes advantage of both simulated annealing and deterministic learning algorithms. The former algorithm is more reliable since it locates a more likely global minimum, but it is slow. The latter algorithm is fast but less reliable, and it can converge to a local minimum. There are many practical uses for the above proposed textural analysis tool such as remote sensing, mineralogical analysis, and medical image processing. In this paper, successful applications of the present stochastic model to synthetic texture images and MRI tongue and brain images are described.