Double Enhanced Solution Quality Boosted RIME Algorithm with Crisscross Operations for Breast Cancer Image Segmentation

IF 4.9 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY Journal of Bionic Engineering Pub Date : 2024-11-11 DOI:10.1007/s42235-024-00590-8
Mengjun Sun, Yi Chen, Ali Asghar Heidari, Lei Liu, Huiling Chen, Qiuxiang He
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Abstract

The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis. Computer-aided medical systems are designed to provide accurate information and reduce human errors, in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes. Multilevel Threshold Image Segmentation (MTIS) is widely favored due to its stability and straightforward implementation. Especially when dealing with sophisticated anatomical structures, high-level thresholding is a crucial technique in identifying fine details. To enhance the accuracy of complex breast cancer image segmentation, this paper proposes an improved version of RIME optimizer EECRIME, denoted as the double Enhanced solution quality Crisscross RIME algorithm. The original RIME initially conducts an efficient optimization to target promising solutions. The double-enhanced solution quality (EESQ) mechanism is proposed for thorough exploitation without falling into local optimum. In contrast, the crisscross operations perform a further local exploration of the generated feasible solutions. The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions. Furthermore, an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma (IDC) histology images. The results demonstrate that the developed model significantly surpasses its competitors, establishing it as a practical approach for complex medical image processing.

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采用十字交叉操作的双重增强型解决方案质量提升 RIME 算法用于乳腺癌图像分割
乳腺癌的发病率居高不下,因此在诊断过程中需要进行精确检测。计算机辅助医疗系统旨在提供准确的信息并减少人为错误,其中准确有效的医学影像分割在提高临床疗效方面发挥着关键作用。多级阈值图像分割(MTIS)因其稳定性和简单易行而广受青睐。特别是在处理复杂的解剖结构时,高级别阈值是识别精细细节的关键技术。为了提高复杂乳腺癌图像分割的准确性,本文提出了一种改进版的 RIME 优化器 EECRIME,即双增强解质量十字交叉 RIME 算法。原始 RIME 最初会针对有希望的解决方案进行高效优化。双增强解质量(EESQ)机制的提出是为了在不陷入局部最优的情况下进行彻底优化。相反,十字交叉操作则对生成的可行解决方案进行进一步的局部探索。在 IEEE CEC2017 基准函数上,EECRIME 的性能与基本算法和高级算法进行了验证。此外,基于 EECRIME 的 MTIS 方法与 Kapur 的熵相结合,被应用于分割乳腺浸润性导管癌(IDC)组织学图像。结果表明,所开发的模型大大超越了竞争对手,成为复杂医学图像处理的实用方法。
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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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