The spread of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) which causes CoronaVirus Disease 2019 (COVID-19) has challenged many countries. To curb the effect of the pandemic requires the development of low-cost and rapid tools for detecting and diagnosing the patients. In this regard, chest X-ray scan images provide a reliable way of detecting the patients. One limitation, however, is the need for experts to analyse the images and identify the cases which can be a burden, when a large number of images are to be processed. The aim of this paper is to propose a method to extract rapidly, from the X-ray images, the regions in which there exist indications of COVID-19 infection. To identify the regions, image segmentation is required which is performed in this paper with a novel optimization algorithm. The proposed optimization algorithm uses probabilistic representation for the solutions. To improve the optimization process, we propose a diversity preserving operator. For multi-level image thresholding via optimization algorithms, different fitness functions have been proposed in the literature. In the proposed method in this paper, we use three fitness functions to benefit from the advantages of all. A fitness swapping scheme is proposed which swaps between the fitness functions in the optimization process. Also, a diversity preserving operator is proposed in this paper which compares the individuals and reinitializes the similar ones to inject diversity in the population. The proposed algorithm is tested on a number of COVID-19 benchmark images and experimental analysis suggest better performance for the proposed algorithm.

导致 2019 年冠状病毒病 (COVID-19) 的严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 的传播给许多国家带来了挑战。为了遏制大流行的影响，需要开发低成本、快速的工具来检测和诊断患者。在这方面，胸部 X 射线扫描图像提供了检测患者的可靠方法。然而，一个限制是需要专家分析图像并识别情况，当要处理大量图像时，这可能会成为一种负担。本文的目的是提出一种从 X 射线图像中快速提取存在 COVID-19 感染迹象的区域的方法。为了识别区域，需要进行图像分割，本文使用一种新颖的优化算法来执行图像分割。所提出的优化算法使用概率表示来求解。为了改进优化过程，我们提出了多样性保留算子。对于通过优化算法进行多级图像阈值处理，文献中提出了不同的适应度函数。在本文提出的方法中，我们使用三个适应度函数来受益于所有函数的优点。提出了一种适应度交换方案，该方案在优化过程中在适应度函数之间进行交换。此外，本文提出了一种多样性保留算子，该算子比较个体并重新初始化相似的个体以在种群中注入多样性。所提出的算法在许多 COVID-19 基准图像上进行了测试，实验分析表明所提出的算法具有更好的性能。