Iterative segmentation algorithms using morphological operations

The authors describe some iterative segmentation algorithms that combine statistical constraints represented in Markov random field models with deterministic constraints imposed by morphological operations. The goal is to produce segmentations that have high probability according to the Markov model and are smooth in the sense of being morphologically open and/or closed. The authors first present several algorithms for binary images, including one that produces a segmentation in which the set of one's is both open and closed. The latter algorithm is then extended to the case of multiregion images to produce a segmentation in which each region is open and closed.<>