Multilevel thresholding segmentation of medical images using the Crested Porcupine Optimizer with Enhanced Solution Quality and Gaussian distribution: Applications to liver, COVID-19, and brain diseases

Accurate liver, COVID-19, and brain disease diagnosis is crucial for effective medical treatment and improved patient outcomes. In Computer-Aided Diagnosis (CAD) systems, segmentation is the foundational step, which plays a pivotal role in accurately delineating regions of interest for subsequent analysis. Among various techniques, multilevel thresholding segmentation is a specialized approach for processing medical images. However, its computational complexity and challenges in achieving satisfactory segmentation results limit its widespread application. To address these issues, this paper proposes an Enhanced Crested Porcupine Optimizer (ECPO) tailored for multilevel thresholding in medical image segmentation. The ECPO integrates two novel strategies: Enhanced Solution Quality (ESQ) and Gaussian Distribution, improving the exploration and exploitation capabilities of the original Crested Porcupine Optimizer (CPO). The optimization performance of ECPO is rigorously evaluated on 12 classical benchmark functions using CEC’2022 test functions, demonstrating superior results compared to CPO and other state-of-the-art algorithms. Subsequently, the ECPO is applied to segmenting medical images from three datasets focusing on liver cancer, COVID-19, and brain diseases. Utilizing Otsu and Kapur methods. Experimental results indicate that ECPO achieves the best segmentation outcomes in terms of fitness values, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The experimental results reveal that ECPO achieves the most accurate and effective segmentation outcomes across all datasets, outperforming other competitive algorithms. These findings underscore the potential of ECPO as a robust and efficient solution to the multilevel thresholding segmentation challenges in medical imaging.