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.

This study presents a novel method that utilizes Harris Hawks Optimization (HHO) combined with dynamic colormap visualization to enhance the quality of lung CT scan segmentation. The Harris Hawks optimization algorithm is a swarm-based method used to enhance multi-level thresholding for image segmentation, hence facilitating the identification of regions of interest (ROIs) in medical images. An analysis of different colormap schemes, including Accent, Gray, Hot, Inferno and Jet, was conducted to improve the visualization of segmented images.The experimental results show the efficiency of the HHO algorithm from the segmentationaccuracy perspective as compared to the conventional optimization techniques using publicly available datasets from the Cancer Imaging Archive. In particular, the average SSIM was above 98% while the Jaccard Index was more than 90%. The expert evaluation confirms earlier findings that using the HHO algorithm with the Inferno colormap, particularly with four or five thresholds, achieves optimal image clarity and diagnostic value for clinical purposes. In addition, the method provides a promising way to enhance diagnostic precision and treatment strategies for lung diseases, making it highly valuable for pulmonaryhealthcare, particularly in urgent scenarios such as pandemics.