Binary and ternary flows for image segmentation

A novel region-based approach to snakes is introduced in this paper for the segmentation of images composed of two or three types of regions where each region may be distinguished by a given statistic. The basic idea behind this technique is to formulate curve evolutions which separate two or more values of a predetermined set of statistics computed over geometrically determined subsets of the image data. Our methodology provides a natural framework for incorporating both global and local image information in the active contour motion while avoiding the use of image derivatives. As such, this technique possesses a robustness to noise which is noncharacteristic of most edge-based snake algorithms.