A fuzzy-rule-based image enhancement method for medical applications

Using fuzzy set theory, we develop a fuzzy rule-based system to perform some of the most common tasks of image enhancement: removing impulsive noise; smoothing nonimpulsive noise; and enhancing edges. Three different filters for each task and the selection criteria based on local information are derived. The selection criteria constitute the antecedent clauses of the fuzzy rules, and the corresponding filters constitute the consequent clauses of the fuzzy rules. The overall result of the fuzzy rule-based system is computed as the combination of the results of the individual filters, where each result contributes to the degree that the corresponding antecedent clause is satisfied. This approach gives us a powerful and flexible image enhancement paradigm. We present results on several types of images such as retinal and chromosome images