Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images

• Published in: Displays Vol. 84; p. 102799
• Main Authors: Shi, Jinge, Chen, Yi, Wang, Chaofan, Heidari, Ali Asghar, Liu, Lei, Chen, Huiling, Chen, Xiaowei, Sun, Li
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Image segmentation; Magnetic liquid climbing; Non-local means 2D histogram; Topological mapping; Whale optimization algorithm; Magnetic liquid climbing; Image segmentation; Topological mapping; Whale optimization algorithm; Non-local means 2D histogram
• ISSN: 0141-9382
• DOI: 10.1016/j.displa.2024.102799

•This study proposed a variant of the WOA called GTMWOA.•This study added three mechanisms to improve the performance of GTMWOA.•GTMWOA tested on the IEEE CEC 2017 benchmark functions.•GTMWOA demonstrated its good performance in multi-threshold segmentation. Lupus Nephritis (LN) has been considered as the most prevalent form of systemic lupus erythematosus. Medical imaging plays an important role in diagnosing and treating LN, which can help doctors accurately assess the extent and extent of the lesion. However, relying solely on visual observation and judgment can introduce subjectivity and errors, especially for complex pathological images. Image segmentation techniques are used to differentiate various tissues and structures in medical images to assist doctors in diagnosis. Multi-threshold Image Segmentation (MIS) has gained widespread recognition for its direct and practical application. However, existing MIS methods still have some issues. Therefore, this study combines non-local means, 2D histogram, and 2D Renyi’s entropy to improve the performance of MIS methods. Additionally, this study introduces an improved variant of the Whale Optimization Algorithm (GTMWOA) to optimize the aforementioned MIS methods and reduce algorithm complexity. The GTMWOA fusions Gaussian Exploration (GE), Topology Mapping (TM), and Magnetic Liquid Climbing (MLC). The GE effectively amplifies the algorithm’s proficiency in local exploration and quickens the convergence rate. The TM facilitates the algorithm in escaping local optima, while the MLC mechanism emulates the physical phenomenon of MLC, refining the algorithm’s convergence precision. This study conducted an extensive series of tests using the IEEE CEC 2017 benchmark functions to demonstrate the superior performance of GTMWOA in addressing intricate optimization problems. Furthermore, this study executed an experiment using Berkeley images and LN images to verify the superiority of GTMWOA in MIS. The ultimate outcomes of the MIS experiments substantiate the algorithm’s advanced capabilities and robustness in handling complex optimization problems.