Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Lupus nephritis (LN) is one of the most common and serious clinical manifestations of systemic lupus erythe-matosus (SLE), which causes serious damage to the kidneys of patients. To effectively assist the pathological diagnosis of LN, many researchers utilize a scheme combining multi-threshold image segmentation (MIS) with metaheuristic algorithms (MAs) to classify LN. However, traditional MAs-based MIS methods tend to fall into local optima in the segmentation process and find it difficult to obtain the optimal threshold set. Aiming at this problem, this paper proposes an improved water cycle algorithm (SCWCA) and applies it to the MIS method to generate an SCWCA-based MIS method. Besides, this MIS method uses a non-local means 2D histogram to represent the image information and utilizes Renyi's entropy as the fitness function. First, SCWCA adds a sine initialization mechanism (SS) in the initial stage of the original WCA to generate the initial solution to improve the population quality. Second, the covariance matrix adaptation evolution strategy (CMA-ES) is applied in the population location update stage of WCA to mine high-quality population information. To validate the excellent performance of the SCWCA-based MIS method, the comparative experiment between some peers and SCWCA was carried out first. The experimental results show that the solution of SCWCA was closer to the global optimal solution and can effectively deal with the local optimal problems. In addition, the segmentation experiments of the SCWCA-based MIS method and other equivalent methods on LN images showed that the former can obtain higher-quality segmented LN images.

Automated summary

This study proposes an improved water cycle algorithm and applies it to multi-threshold image segmentation to achieve higher-quality segmentation of lupus nephritis images.