A parallel marker based watershed transformation

The parallel watershed transformation used in grayscale image segmentation is reconsidered on the basis of markers. The goal is to reduce the typical over segmentation by decreasing the number of catchment basins produced by flooding. Assimilating the set of basins with a weighted neighborhood graph and computing the minimum spanning forest in which every tree is rooted at a marked vertex, all non-marked regions in each tree are incorporated in the root region of the tree. A log₂N distributed message passing algorithm performing the above stated goal on N processors is presented. Two merits of the parallel algorithm are worth of mentioning: first, the local detection of the catchment basins conforming to the watershed principle (which strongly depends on the history of the region growth), with an extremely low communication traffic; and second, the parallel computation of the Boruvka like minimum spanning forest with the constraint that any tree contains exactly one marker. Evaluation of a Cray T3D implementation under the message passing interface (MPI) is included