Image segmentation by multi-level thresholding using genetic algorithm with fuzzy entropy cost functions

In this paper, we describe three new soft computing methods for segmentation of both gray level and color images by using a fuzzy entropy based cost function for the genetic algorithm. The presented methods allow us to find optimized set of parameters for a predefined cost function. Particularly, we found the optimum set of membership functions by maximizing the fuzzy entropy and based on the membership functions. Experimental results of thresholding and comparison of SSIM of thresholded images with different techniques are presented. Results show that the offered method can reliably segment and give better thresholds then Otsu Multi-Level thresholding.