IMAGE SEGMENTATION

Abstract

Fuzzy C-Means (FCM) is one of the popular techniques used for segmenting sci- entific images. It is suggested in the literature to use intuitionistic fuzzy c-means (IFCM), which is based on the notion of intuitionistic fuzzy sets (IFSs), to handle the ambiguity and uncertainty related to 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, the picture fuzzy Euclidean distance is swapped out for the Manhattan distance (City Block Distance), as Manhattan distance produces significantly better noise suppres- sion.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. We can utilize the distance measure and dissimilarity between fuzzy sets to calculate the difference between two fuzzy sets or intuitionistic sets as it can be used for pattern recognition, and image segmentation.Results show that the proposed method gives the better result.