Semi-locally adaptive models have appeared in medical imaging literature in the past years. In particular,
generalized scale models (or g-scale for short) have been introduced to effectively overcome the shape, size, or
anisotropic constraints imposed by previous local morphometric scale models. The g-scale models have shown
interesting theoretical properties and an ability to drive improved image processing as shown in previous works.
In this paper, we present a noise-resistant variant for g-scale set formation, which we refer to as stabilized scale (s-scale)
because of its stabilized diffusive properties. This is a modified diffusion process wherein a well-conditioned
and stable behavior in the vicinity of boundaries is defined. Yet, s-scale includes an intensity-merging dynamics
behavior in the same manner as that found in the switching control of a nonlinear system. Basically we introduce,
in the evolution of the diffusive model, a behavior state to drive neighboring voxel intensities to larger and larger
iso-intensity regions. In other words, we drive our diffusion process to a coarser and coarser piecewise-constant
approximation of the original scene. This strategy reveals a well-known behavior in control theory, called sliding
modes. Evaluations on a mathematical phantom, the Brainweb, MR and CT data sets were conducted. The
s-scale has shown better performance than the original g-scale under moderate to high noise levels.