Using Statistical Pattern Recognition Techniques to Control Variable Conductance Diffusion

We present an approach for controlling relaxation parameters in variable conductance diffusion. This approach incorporates a Bayesian classifier to perform a partial labeling of an image, followed by a diffusion step. Conductance values between pixels are controlled by statistical measurements made of the partial classification. Several iterations follow, interleaving partial labeling with diffusion steps until a convergence or stopping criterion is met. The method is suitable for performing diffusion within multi-valued images. It consistently controls relaxation parameters, even in the presence of noise. The method is presented along with results on phantom and MR images.