ADSPAO: Enhanced artemisinin optimization for multi-threshold segmentation of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) poses a significant threat to global public health, with its diagnosis heavily reliant on accurate segmentation of medical computed tomography (CT) images. However, current segmentation methods suffer from limited optimization efficiency, accuracy, and high computational costs. To address these issues, this paper proposes an improved artemisinin optimization algorithm (ADSPAO), which incorporates spiral propagation (SP) and adaptive delivery (AD) strategies to enhance local exploitation and global exploration, respectively. Experimental comparisons on the IEEE CEC2017 benchmark show that ADSPAO exhibits superior robustness and stability across various function types. Furthermore, the algorithm is applied to multi-threshold segmentation of COPD CT images using Renyi entropy as the fitness function. Results demonstrate that ADSPAO achieves better segmentation performance in terms of peak signal to noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) compared to other methods, offering an efficient and reliable solution for medical image segmentation.