Extended fuzzy rules for image segmentation

The generic fuzzy rule-based image segmentation technique (GFRIS) does not produce good results for non-homogeneous reg’ons that possess abrupt changes in pixel intensity, because if fails to consider two important properties of perceptual grouping, namely surroundedness and connectedness. In this paper a new technique called exfended fuzzy rules for image segmentation (EFRIS) isproposed, which includes a second rule to that defined already in GFRIS, fhat incorporates both the surroundedness and connectedness properties of a region ’s pixels. This additional rule is based on a spilt and merge algorithm and refines the ou@ut from the GFRIS technique. Two different classes of image, namely light intensify and medical X rays are empirically used to assess the performance of the new technique. Quantitative evaluation of the performance of EFRIS is discussed and contrasted with GFRIS using one of the standard segmentation evaluation methods. Overall, EFRIS exhibits significantly improved results compared with the GFRIS approach.