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一种基于模糊聚类和增强操作员的新方法,用于医疗图像对比度增强
1Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, 010000, Vietnam.
Current medical imaging
|July 25, 2023
概括
一个新的算法,基于集群增强 (MIECE) 的医疗图像增强,提高了医疗图像质量. MIECE有效地增强暗物体,在对比增强方面表现优于最近的方法.
科学领域:
- 医疗图像处理 医学图像处理
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 图像增强对于提高图像质量至关重要.
- 传统的方法在医疗图像中难以增强黑暗和明亮的区域.
- 现有的技术经常使用全局方法,在复杂的照明条件下限制有效性.
研究的目的:
- 介绍了一种用于医学图像增强的新算法.
- 解决当前处理不同图像强度的方法的局限性.
- 改善医学扫描中的细节对比度和可见性.
主要方法:
- 开发了一个新的算法:基于集群增强 (MIECE) 的医学图像增强.
- 采用模糊聚类来分组图像像素.
- 使用增强运算符应用了一个基于集群的增强子算法.
- 聚合增强的灰色水平加权的集群成员资格值.
主要成果:
- MIECE在医学图像数据上进行了测试.
- 实验结果显示,与SGHIE (2017年),Ying (2017年) 和KindD++ (2021年) 相比,其性能优越.
- 该算法有效地增强了医疗图像中的暗物体.
结论:
- MIECE为医疗图像对比度增强提供了一种有效的方法.
- 拟议的算法显示了显著的改进,特别是用于增强暗区域.
- 与现有的医学图像增强技术相比,MIECE 是一个进步.
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