A Novel Optimization Algorithm for Otsu's Entropy-Based Multi-Level Thresholding for Image Segmentation

Abstract

In applications involving image processing, segmentation is an essential stage. This procedure divides the image's pixels into various classes, enabling the examination of the scene's objects. Finding the ideal collection of thresholds to correctly segment each image is a challenge that multilevel thresholding solves with ease. The optimal thresholds can be found using methods like Otsu's between-class variance or kapur's entropy, but they are computationally costly when there are more than two thresholds. This study presents a novel meta-heuristic algorithm, Election-Based Optimization Algorithm (EBOA) to discover the optimal threshold configuration with Otsu as the objective function, to solve this kind of problem. The obtained results proved better in WPSNR, PSNR, SSIM, FSIM and misclassification error and segmented image quality when compared with existing algorithms.