Many-objectives multilevel thresholding image segmentation using Knee Evolutionary Algorithm

Abstract

Image segmentation is one of the popular tasks in image processing that can be used in several applications. For that, there are several methods have been proposed for image segmentation; in which, these methods aim to minimize or maximize single objective (SO) function to find the optimal threshold to separate the image into the optimal number of regions. While a few of the image segmentation methods consider multi-objective functions which aim to find the optimal solutions that can reduce the conflict among the different objectives. However, these methods have some limitations as decreasing their performance when the number of objectives is increasing. Due to increasing the number of non-dominated solutions, so, there isn’t pressure towards the Pareto front. Therefore, this paper proposes an alternative image segmentation method using many-objective optimization (MaOP) algorithms considering seven objective functions. One of the most competitive MaOPs is called the Knee Evolutionary Algorithm (KnEA) which used to find the set of Pareto optimal solutions for seven objective functions to improve the image segmentation. The proposed KnEA is evaluated using a set of six images tested at six different levels of threshold, and its performance is compared with other MaOP methods. The experimental results show that the KnEA method has a better approximation to the optimal Pareto fronts (PFs) than the other MaOPs method in terms of the quality of the segmented image such as the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and the computational time. As well as, the quality of PFs is measuring using the hypervolume, coverage and spacing indicators.