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. 

FMMIS learning algorithm

1. Initialisation
2. Hyperbox Expansion
3. Hyperbox Ovelap Test
4. Hyperbox Contraction
5. Fine-tuning Hyperbox Expansion
6. Hyperbox Merging