Fuzzy min-max neural network for image segmentation (FMMIS)

• The FMMIS method places hyperboxes defined in the 2D geometric space by pairs of min-max points for each spatial coordinate of the image (rectangular boxes in the case of 2D images).
• Each hyperbox fuzzy set has an associated membership function that describes the degree of membership (spatial proximity) of a given pixel to a hyperbox in the [0,1] interval.
• When an input pattern (new seed) is presented, the hyperbox with the highest degree of membership is found and expanded to enclose the input pattern.
• The hyperbox expansion is accepted only if the region contained by the expanded hyperbox is similar in colour to the region enclosed by the hyperbox before the expansion.