A generic fuzzy rule based image segmentation algorithm

Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, the corresponding parameters being manually determined. The net result is that the overall performance of the segmentation technique is very sensitive to parameter value selections. This paper addresses these issues by introducing a generic fuzzy rule based image segmentation (GFRIS) algorithm, which is both application independent and exploits inter-pixel spatial relationships. The GFRIS algorithm automatically approximates both the key weighting factor and threshold value in the definitions of the fuzzy rule and neighbourhood system, respectively. A quantitative evaluation is presented between the segmentation results obtained using GFRIS and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. The results demonstrate that GFRIS exhibits a considerable improvement in performance compared to both FCM and PCM, for many different image types.

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