Anti-geometric diffusion for adaptive thresholding and fast segmentation

In this paper, we utilize an anisotropic diffusion model, which we call the anti-geometric heat flow, for adaptive thresholding of bimodal images and for segmentation of more general greyscale images. In a departure from most anisotropic diffusion techniques, we select the local diffusion direction that smears edges in the image rather than seeking to preserve them. In this manner, we are rapidly able to detect and discriminate between entire image regions that lie nearby, but on opposite sides, of a prominent edge. The detection of such regions occurs during the diffusion process rather than afterward, thereby side-stepping the most notorious problem associated with diffusion methods, namely, "When should you stop diffusing?" We initially outline a procedure for adaptive thresholding, but ultimately show how this model may be used in a region splitting procedure which, when combined with energy based region merging procedures, provides a general framework for image segmentation. We discuss a fast implementation of one such framework and demonstrate its effectiveness in segmenting medical, military, and scene imagery.