找最优小波降噪参数，让脑部MRI更清晰
Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks
- 用小波变换分层去噪，试了不同阈值和分解层数
- biorthogonal bior6.8小波在2-3层时效果最好
- 适合临床医生看图诊断，也帮图像增强不放大噪声
医学影像如磁共振成像（MRI）、计算机断层扫描（CT）和超声对现代医疗诊断与治疗至关重要。但图像采集和处理过程中的噪声会降低图像质量，掩盖关键诊断信息，影响临床判断。此外，直方图均衡等增强技术可能放大盐粒状噪声等伪影。本研究系统评估基于小波变换的去噪方法，旨在确定最优的阈值、分解层数与小波类型组合，以提升去噪性能并改善诊断准确性。实验在多种噪声条件下进行，结果表明：bior6.8双正交小波配合通用阈值，在分解层数2-3时表现最佳，能显著降噪，同时有效保留重要解剖结构和临床特征。
原文摘要 · Abstract (English)
Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, including salt-and-pepper distortions. This study investigates wavelet transform-based denoising methods for effective noise mitigation in medical images, with the primary objective of identifying optimal combinations of threshold values, decomposition levels, and wavelet types to achieve superior denoising performance and enhanced diagnostic accuracy. Through systematic evaluation across various noise conditions, the research demonstrates that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2-3 consistently achieves optimal denoising performance, providing significant noise reduction while preserving essential anatomical structures and diagnostic features critical for clinical applications.
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