In this paper we provide rigorous proof for the convergence of an iterative
voting-based image segmentation algorithm called Active Masks. Active Masks
(AM) was proposed to solve the challenging task of delineating punctate
patterns of cells from fluorescence microscope images. Each iteration of AM
consists of a linear convolution composed with a nonlinear thresholding; what
makes this process special in our case is the presence of additive terms whose
role is to "skew" the voting when prior information is available. In real-world
implementation, the AM algorithm always converges to a fixed point. We study
the behavior of AM rigorously and present a proof of this convergence. The key
idea is to formulate AM as a generalized (parallel) majority cellular
automaton, adapting proof techniques from discrete dynamical systems