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通过对立的蛇优化算法精确的多级值图像分割:肝脏疾病的真实病例.

Essam H Houssein¹, Nada Abdalkarim¹, Kashif Hussain²

¹Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in biology and medicine

|January 7, 2024

概括

这项研究引入了一种增强的蛇优化 (SO) 算法与基于对立的学习 (OBL),称为SO-OBL,用于CT扫描中准确的肝病细分. SO-OBL模型展示了计算机辅助诊断系统的卓越性能和效率.

关键词:

全球优化全球优化图像细分图像细分图像细分肝脏疾病肝脏疾病超听证学是一种超听证学.多级值设置多级值设置蛇优化算法蛇优化算法

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科学领域:

医学成像分析医学成像分析
人工智能在医学中的应用

背景情况:

肝脏疾病是全球主要的健康问题,需要准确的诊断工具.
计算机辅助诊断 (CAD) 系统需要精确的肝脏细分从CT扫描有效的治疗.
肝脏细分的挑战包括不一致的器官存在和模糊的边界.

研究的目的:

开发一个增强的蛇优化 (SO) 算法,与基于对立的学习 (OBL) 集成,称为SO-OBL,用于改善肝病细分.
在全球优化和多层次图像分割任务中评估SO-OBL算法的性能.
为计算机辅助诊断 (CAD) 系统创建一个先进的肝脏细分模型.

主要方法:

一个增强的蛇优化 (SO) 算法,结合了基于对立的学习 (OBL) 被开发 (SO-OBL).
该SO-OBL算法与使用CEC'2022测试函数的11个最先进的元启发算法进行了基准测试.
使用SO-OBL算法和优化的多级值技术 (Otsu的函数) 构建了一种肝病细分模型.

主要成果:

与现有的元启发算法相比,SO-OBL算法在全球优化方面表现出了卓越的性能.
肝脏细分模型实现了高精度的FSIM=0.947,SSIM=0.941和PSNR=24.876. 这样,肝脏细分模型获得了高精度.
该模型表现出高效率,执行时间短0.281秒.

结论:

拟议的SO-OBL算法有效地解决了来自CT扫描的肝脏细分方面的挑战.
开发的细分模型显示了计算机辅助诊断 (CAD) 系统中准确高效诊断的巨大潜力.
这项研究有助于推进用于检测肝病的医学图像分析.