I will talk about the new active mask (AM) framework and an algorithm for segmentation of digital images, particularly those of punctate patterns from fluorescence microscopy. The AM segmentation framework is suited for digital images. It is based on a local majority voting-based scheme, and can incorporate different forms of the voting function as well as several different functions to skew the voting to obtain a meaningful segmentation result. This framework has multiresolution and multiscale techniques built into it and can be instantiated to segment data of any dimension. We demonstrate the efficacy of the AM through an algorithm for segmenting punctate patterns of cells in fluorescence microscope images. While the theory opens up interesting vistas for research and development, the results demonstrate AM's utility in practice.