IMAGE SAGMANTATION

Abstract

One well-liked method for dividing up scientific photos is fuzzy C-Means (FCM).The use of intuitionistic fuzzy C-Means (IFCM), which is based on the idea of intuition istic fuzzy sets (IFSs), is advocated in the literature as a way to deal with the ambiguity and uncertainty associated with real data.The hesitation and member ship degrees are used to determine the objective function.However, FCM is used to achieve the approximate answer rather than analytically computing the objective function. Even though there are numerous variations of intuitionistic fuzzy set the ory, all of them struggle with the issue of noise in images during the segmentation process. In order to address this issue, we have proposed using a picture fuzzy set theoretic approach, which improves the data’s ability to be represented and aids in handling the noise structures present in the image In our proposed work, we have added algorithm of FCM , PFCM, and some applications. The method was applied to a fake image that had been ”Gaussian” and ”salt and pepper” distorted. Partition efficiency, average segmentation accuracy (ASA), and dice score (DS) were the performance metrics used. In order to determine the difference between two fuzzy sets or intuitionistic sets, we can use the distance measure and the dissimilarity between them, which are both employed in pattern recognition and image segmentation.