Image Segmentation using Global and Local Fuzzy Statistics

Abstract

In this paper, criterion optimization based image thresholding techniques to perform segmentation using global and local fuzzy statistics are presented. The global and local fuzzy statistics considered for an image are the fuzzy histogram and fuzzy co-occurrence matrix of the image, respectively. A novel way of adapting the membership function, which is required to calculate the fuzzy statistics, to the local nature of the corresponding crisp first-order statistic (histogram) is suggested. The fuzzy statistics of an image obtained using such an adaptive membership function are called adaptive fuzzy statistics. Experimental results of various image segmentation techniques using crisp, fuzzy and the proposed adaptive fuzzy statistics are given. A comparative study demonstrating the usefulness of fuzzy statistics in image segmentation and the effectiveness of adapting the membership function in order to determine the fuzzy statistics is presented