In this paper, we describe a new region-based approach to active contours for segmenting images composed of two or three types of regions characterizable by a given statistic. The essential idea is to derive curve evolutions which separate two or more values of a predetermined set of statistics computed over geometrically determined subsets of the image. Both global and local image information is used to evolve the active contour. Image derivatives, however, are avoided, thereby giving rise to a further degree of noise robustness compared to most edge-based snake algorithms. 1

In this work we modify the couple d curve evolution approach to snakes presente dby the authors in pre-vious work for bimodal and trimodal imagery through the intr oduction of glob al constr aints. The key idea, as b efor e, is to derive curve evolution equations which \pull apart " the values