Image processing is among the significant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from different scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very effective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for different image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic firefly algorithm (FA), differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony optimization (ABC), etc. Moreover, artificial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms.

图像处理是当前场景中重要的增长领域之一。它由一组通常用于增强从不同场景获得的原始图像的技术组成。图像分割是图像分析和预处理的重要步骤。在工作过程中，标准的多级阈值方法由于其计算成本低、可靠性、收敛时间缩短和精度高而非常有效。受自然启发的优化方法在图像处理中起着至关重要的作用。已经针对不同的图像处理应用提出了几种优化程序。这些优化技术可以提高图像分割、图像恢复、边缘检测、图像增强、模式识别、图像生成、图像阈值化、和图像融合算法。本文概述了几种元启发式萤火虫算法 (FA)、差分进化 (DE)、粒子群优化 (PSO)、遗传算法 (GA)、人工蜂群优化 (ABC) 等。此外，人工神经网络 (ANN) ) 和其他机器学习技术（自然或生物启发）在图像分割应用程序及其算法的上下文中进行了讨论。