Digital image processing techniques and algorithms have become a great tool 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 the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu’s method to effectively reduce the required computational cost while maintaining optimal outcomes. 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 can 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.

Medical image segmentation algorithms play a crucial role in assisting healthcare professionals with disease identification, research, and diagnosis. Numerous digital image segmentation methods have been developed, with multilevel thresholding techniques consistently outperforming others in terms of evaluation metrics. The standard algorithms include classical statistical methods, such as the Otsu and Kapur methods, which yield highly accurate results. However, when applied to multilevel thresholding, these methods incur significant computational costs, presenting an optimization challenge. In this work, a set of well-known optimization algorithms is integrated with Otsu’s method to assess their effectiveness in reducing computational demands while preserving optimal segmentation quality. Experiments are conducted on publicly available datasets, including the TCIA dataset, particularly the COVID-19-AR collection. This work evaluates the performance of each optimization algorithm in combination with Otsu’s method, highlighting those that achieve substantial reductions in computational cost and convergence time while maintaining a competitive level of segmentation quality.