AFSRIME: A high-performance lupus nephritis image segmentation method using adaptive rime factor-enhanced rime optimization and foraging storage strategy

• Published in: Displays Vol. 90; p. 103134
• Main Authors: Huang, Jinpeng, Heidari, Ali Asghar, Liu, Lei, Zhang, Qian, Chen, Huiling, Chen, Xiaowei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2025
• Subjects: Foraging and storage strategy; Lupus nephritis; Multi-threshold image segmentation; Rime optimization algorithm; Multi-threshold image segmentation; Foraging and storage strategy; Lupus nephritis; Rime optimization algorithm
• ISSN: 0141-9382
• DOI: 10.1016/j.displa.2025.103134

•A variant AFSRIME of RIME was proposed, which integrates the foraging storage strategy and proposes an adaptive rime factor.•AFSRIME improves the convergence accuracy of RIME, alleviates the limitation of RIME being easily trapped in local optima, and enhances the ability of RIME to utilize population information.•AFSRIME has excellent performance on the IEEE CEC 2017 benchmark function.•AFSRIME improves the performance of RIME in multi threshold image segmentation of lupus nephritis, and it also has advantages compared to other advanced algorithms. Lupus nephritis (LN), a severe kidney complication of systemic lupus erythematosus, necessitates prompt diagnosis and treatment to prevent kidney failure and other serious health consequences. To assist more accurately in the pathological diagnosis of LN, researchers have explored an innovative method that combines multi-threshold image segmentation and a metaheuristic algorithm for LN classification. However, it is worth noting that the traditional multi-threshold image segmentation technique based on a metaheuristic algorithm often faces the dilemma of falling into local optimal solutions during the image segmentation process, making determining the optimal threshold set quite difficult. To address this issue, we propose the AFSRIME, a variant of the rime optimization algorithm (RIME). Specifically, we modify the rime factor of RIME and introduce an adaptive rime factor, replacing the optimal global position with the optimal position of each agent. This modification aims to alleviate the convergence stagnation problem encountered by RIME in its later updates and mitigate its propensity to fall into local optima. We further incorporated a foraging and storage strategy to enhance information sharing within the RIME populations and fully exploit the intra-group information. To verify the performance of AFSRIME, we conducted a series of tests using the IEEE CEC 2017 benchmark functions and compared them with multiple cutting-edge algorithms. The validation results indicated AFSRIME’s impressive performance. Finally, we applied AFSRIME to multi-threshold segmentation of LN images using Rényi’s entropy non-local means two-dimensional histogram segmentation technique. The results show that AFSRIME enhanced RIME’s multi-threshold segmentation capability on LN images, demonstrating a distinct advantage compared to other advanced algorithms. These findings underscore AFSRIME’s promising future in LN image segmentation applications.