Digital image processing techniques and algorithms have become a great tools to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are considered to be the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results yet they suffer from high computation costs when multilevel thresholding is required. To address this issue the recent Harris hawks optimization (HHO) algorithm is combined with Otsu’s method for multilevel image thresholding purposes in this work. The proposed approach is tested on a publicly available imaging datasets , including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach is able to achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.