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Multi-threshold image segmentation using a boosted whale optimization: case study of breast invasive ductal carcinomas

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS ANd APPLICATIONS 2024年第10期27卷 14891-14949页

作者： Shi, Jinge Chen, Yi Cai, Zhennao Heidari, Ali Asghar Chen, Huiling He, Qiuxiang Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Med Univ Affiliated Hosp 1 Dept Pathol Wenzhou Zhejiang Peoples R China

Medical imaging is essential in modern healthcare because it assists physicians in the diagnosis of cancer. Various tissues and features in medical imaging can be recognized using image segmentation algorithms. This feature makes it possible to pinpoint and define particular areas, which makes it easier to precisely locate and characterize anomalities or lesions for cancer diagnosis. Among cancers affecting women, breast cancer is particularly prevalent, underscoring the urgent need to improve the accuracy of image segmentation for breast cancer in order to assist medical practitioners. Multi-threshold image segmentation is widely acknowledged for its direct and effective characteristics. In this context, this paper suggests a refined whale optimization algorithm to improve the segmentation accuracy of breast cancer data. This algorithm optimizes performance by combining a quantum phase interference mechanism and an enhanced solution quality strategy. This work compares the method with classical, homogeneous, state-of-the-art algorithms and runs experiments on the IEEE CEC2017 benchmark to validate its practical optimization performance. Furthermore, a multi-threshold image segmentation algorithm-based image segmentation technique is presented in this study. The Berkeley segmentation dataset and the breast invasive ductal carcinomas segmentation dataset are segmented using the approach using a non-local means two-dimensional histogram and Renyi's entropy. Experimental results demonstrate the excellent performance of this segmentation method in image segmentation applications across both low and high threshold levels. As a result, it emerges as a valuable image segmentation technique with practical applications.

关键词： Whale optimization algorithm Quantum phase Image segmentation 2d Renyi's entropy non-local means 2d histogram

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Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis

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BIOMEdICAL SIGNAL PROCESSING ANd CONTROL 2024年第PartA期96卷

作者： Shi, Jinge Chen, Yi Cai, Zhennao Heidari, Ali Asghar Chen, Huiling Chen, Xiaowei Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Med Univ Affiliated Hosp 1 Dept Rheumatol & Immunol Wenzhou 325000 Peoples R China

Lupus nephritis (LN) is a severe renal complication of systemic lupus erythematosus (SLE), characterized by kidney inflammation and damage, often leading to significant morbidity and mortality. Thus, aiding clinicians in the timely diagnosis of LN is imperative, with medical imaging serving as an essential adjunctive diagnostic tool.. Image segmentation techniques discriminate various tissues and structures within medical images. This capability allows for identifying and delineating specific regions within images, facilitating precise localization and characterization of anomalies or pathological conditions to aid physicians in their diagnosis. Multi-threshold Image Segmentation (MTIS) is widely recognized for its direct and practical application in medical image segmentation. Hence, this study introduces an enhanced Whale Optimization Algorithm (CVWOA) to enhance the accuracy of image segmentation. The proposed CVWOA combines convex local directed search and vortex rotation mechanisms, significantly improving the algorithm's overall optimization performance. To evaluate the performance of CVWOA, experiments were conducted using CVWOA on benchmark datasets, and comparisons were made with classical and state-of-the-art algorithms. Building upon these findings, the study proposes a CVWOA-based MTIS approach. This method utilizes non-local means two-dimensional histograms in conjunction with Renyi's entropy to perform segmentation on the Berkeley Segmentation dataset and the LN segmentation dataset. Experimental results demonstrate that this segmentation method is both effective and competitively positioned.

关键词： Whale optimization algorithm Image segmentation 2dRenyi's entropy non-local means 2d histogram Lupus Nephritis

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

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dISPLAYS 2024年 84卷

作者： Shi, Jinge Chen, Yi Wang, Chaofan Heidari, Ali Asghar Liu, Lei Chen, Huiling Chen, Xiaowei Sun, Li Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Rheumatol & Immunol Wenzhou 325000 Peoples R China

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.

关键词： Whale optimization algorithm Magnetic liquid climbing Image segmentation Topological mapping non-local means 2d histogram

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