一种改进的多式医疗图像融合方法,使用直觉模糊集和直觉模糊交叉相关性
Maruturi Haribabu1, Velmathi Guruviah1
1School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
这项研究介绍了一种新的直观模糊集基于多式联络医疗图像融合方法. 新方法提高了融合图像质量,以改善医疗诊断,优于现有技术.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 多式医疗图像融合 (MMIF) 融合了各种图像类型,以加强临床诊断.
- 直觉模糊集 (IFS) 在医学图像融合 (MIF) 中提高图像质量.
研究的目的:
- 为多式联络医疗图像融合提出一种新的直观的模糊集合式方法.
- 提高融合医学图像的质量和诊断价值.
主要方法:
- 输入医疗图像被模糊成直觉模糊图像 (IFI).
- 直觉模糊断决定了最佳的会员资格,非会员资格和犹程度.
- 国际金融机构被分解,使用定义的规则进行融合,并进行模糊化以获得最终的图像.
主要成果:
- 与PCA,DWTPCA,CONT,含模糊逻辑的DWT,Sugeno的IFS,Chaira的IFS和PC-NSCT相比,拟议的方法产生了优越的融合图像质量.
- 主观和客观分析证实了融合图像的增强质量.
结论:
- 基于模糊集的MMIF方法显著提高了融合图像的质量.
- 与现有方法相比,这种方法为医学诊断提供了更有效的解决方案.
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