一种用于X射线相位对比成像的新融合方法,基于快速自适应的二维实证模式分解
Zonghan Tian1, Siwei Tao1, Ling Bai1
1State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science & Engineering, Zhejiang University, Hangzhou, China.
Journal of X-ray science and technology
|October 16, 2023
概括
使用 δ-引导快速自适应的二维实证模式分解和形态脉冲合神经网络 (FABEMD-MGPCNN) 的新图像融合方法增强了X射线相对照成像 (XPCI) 和计算机断层扫描 (CT) 图像.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 计算科学 计算科学
背景情况:
- X射线相位对比成像 (XPCI) 将衰减,折射和散射信号分开.
- 图像融合集中了独特的信息,但传统的方法,如波形分解,可以丢失数据.
- 在XPCI中现有的融合方法有局限性.
研究的目的:
- 评估用于XPCI和计算机断层扫描 (CT) 系统的新型图像融合方法.
- 解决现有的XPCI图像融合技术中的数据丢失问题.
- 探索高级图像融合在医学成像中的应用价值.
主要方法:
- 使用快速自适应的二维实证模式分解 (FABEMD) 来防止在图像分解过程中丢失信息.
- 引入了一种新的参数 δ,用于指导高频二维内在模式函数的融合,使用形态梯度脉冲合神经网络 (MGPCNN).
- 采用了对残余图像的能量属性融合策略,并进行了质量保证的预处理/增强.
主要成果:
- 提出的d引导的FABEMD-MGPCNN方法在生物样本的客观评估指标中获得了最高排名.
- 与离散波束转换和异型扩散聚变相比,已证明具有优越的性能.
- 在CT图像上应用时成功保留特征信息,显示预期结果.
结论:
- 导向的FABEMD-MGPCNN方法是XPCI和CT系统中图像融合的可行和优越方法.
- 这种新的方法克服了传统和最新的融合技术的局限性.
- 这些发现表明,在X射线成像中改善诊断准确性的巨大潜力.
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