适应性压缩和重建用于多维医学图像数据:用于增强图像质量的混合算法
Pauline Freeda David1, Suganya Devi Kothandapani2, Ganesh Kumar Pugalendhi3
1Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India.
Journal of imaging informatics in medicine
|December 20, 2024
概括
本研究介绍了医疗图像的自适应性压缩算法,优先考虑关键区域. 经过修改的SPIHT Huffman和EZW方法为增强的诊断分析提供了更高的重建质量.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 数据压缩数据压缩
背景情况:
- 由于关键的诊断细节,医疗图像 (MI) 在压缩过程中需要高保真度.
- 标准压缩方法可能会降低兴趣区域 (ROI) 中的基本信息.
研究的目的:
- 为医疗图像开发一种适应性压缩算法,以保持诊断质量.
- 为了有效地压缩来自MRI,CT和X射线的多维医学图像数据.
主要方法:
- 图像增强使用边缘增强-对比度有限的自适应式直方图平衡 (EE-CLAHE) 和自适应性异型扩散.
- 用自适应期望最大化集群 (AEMC) 将图像分成ROI和非ROI,并使用模糊C-Means (FCM) 和Otsu值进行优化.
- 不同的压缩方案 (Coiflet+Haar,Coiflet+Daubechies,修改的SPIHT Huffman,EZW,SPIHT) 应用于ROI和非ROI.
主要成果:
- 修改后的SPIHT Huffman用于ROI和EZW用于非ROI的组合显示出优越的重建质量.
- 拟议的算法有效地保留了诊断细节,同时减少了医疗图像尺寸.
- 在MRI,CT和X射线模式的实验验证证证了算法的有效性.
结论:
- 适应性压缩对于保持医学成像诊断完整性至关重要.
- 开发的算法为压缩各种医疗图像模式提供了有效的解决方案.
- 针对ROI和非ROI的优化压缩策略提高了整体图像数据管理和分析.
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