The parallel watershed transformation used in gray-scale image segmentation is here augmented to perform with the aid of a priori supplied image cues called markers. The reason for introducing markers is to calibrate a resilient algorithm to oversegmentation. In a hybrid fashion, pixels are first clustered based on spatial proximity and gray-level homogeneity with the watershed transformation. Boundary-based region merging is then effected to condense nonmarked regions into marked catchment basins. The agglomeration strategy works with a weighted neighborhood graph representation of the oversegmented image. The throughput of a parallel Borvka-like minimum spanning forest (MSF) operator, applied on the considered graph, embodies the desired image partition, reasoning that all regions in a tree fuse into a homogeneous area containing a unique marker. Two figures of merit of the parallel algorithm are worth mentioning: the local detection of the catchment basins conforming to the watershed principle (which strongly depends on the history of the regions' growth) and the parallel computation of the Borvka-like MSF which merges, at the same time, partial regions, produced by the local labeling, and nonmarked regions to marked basins. Both modules are designed with great concurrency, locality, and reduced software engineering cost, emerging into a scalable algorithm.