Mask Connectivity by Viscous Closings: Linking Merging Galaxies without Merging Double Stars

Second-generation connectivity opened the path to the use of mask
images to freely define connectivity among the image components. In
theory, any image could be treated as a mask image that defines a
certain connectivity. This creates a new problem in terms of which
image to use. In this paper, clustering masks suitable for the
analysis of astronomical images are discussed. The connectivity
defined by such masks must be capable of preserving faint structures
like the filaments that link merging galaxies while separating
neighboring stars. In this way, the actual morphology of the objects
of interest is kept. This is useful for proper segmentation. We show
that viscous mathematical morphology operators have a superior
performance and create appropriate connectivity masks that can deal
with the characteristic features of astronomical images.