AGPLO-Driven Optimisation for Accurate Segmentation of Papillary Thyroid Carcinoma in Medical Imaging

Abstract

Papillary Thyroid Carcinoma (PTC) is the most prevalent thyroid malignancy, and accurate lesion segmentation is essential for clinical diagnosis and treatment planning. Metaheuristic optimisation algorithms have been widely used in Multi-Threshold Image Segmentation (MTIS), but many existing methods suffer from an imbalance between global exploration and local exploitation. This study aims to develop a robust and well-balanced optimisation algorithm to improve the accuracy and stability of MTIS for PTC images. An Adaptive Guided Polar Lights Optimisation (AGPLO) algorithm is proposed, which incorporates an adaptive phase-shift operator, magnetic guiding convergence, and energy burst exploration mechanisms to dynamically regulate search behaviour. AGPLO was evaluated on the IEEE CEC2017 benchmark suite and applied to Rényi entropy-based MTIS for PTC image segmentation. Experimental results on benchmark functions demonstrate that AGPLO outperforms several original and advanced metaheuristic algorithms in terms of convergence accuracy, stability, and robustness. In PTC image segmentation experiments, AGPLO achieves superior PSNR, SSIM, and FSIM values, producing clearer lesion boundaries and preserving structural details more effectively than comparative methods. The proposed AGPLO provides an effective and reliable optimisation framework for MTIS and shows strong potential for intelligent medical image analysis applications.