  In the absence of a pure noise-free image it is hard to define what noise is,
in any original noisy image, and as a consequence also where it is, and in what
amount. In fact, the definition of noise depends largely on our own aim in the
whole image analysis process, and (perhaps more important) in our
self-perception of noise. For instance, when we perceive noise as disconnected
and small it is normal to use MM-ASF filters to treat it. There is two
evidences of this. First, in many instances there is no ideal and pure
noise-free image to compare our filtering process (nothing but our
self-perception of its pure image); second, and related with this first point,
MM transformations that we chose are only based on our self - and perhaps -
fuzzy notion. The present proposal combines the results of two MM filtering
transformations (FT1, FT2) and makes use of some measures and quantitative
relations on their Size/Intensity Diagrams to find the most appropriate noise
removal process. Results can also be used for finding the most appropriate stop
criteria, and the right sequence of MM operators combination on Alternating
Sequential Filters (ASF), if these measures are applied, for instance, on a
Genetic Algorithm's target function.
