使用特征提取和对抗性扩散模型对超声波图像无色化的探索性研究
Yue Hu1,2, Huiying Xu1,3, Xinzhong Zhu1,3
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China.
Medical physics
|September 25, 2025
概括
这项研究介绍了ADM-ExNet,这是一种基于扩散模型 (DM) 的新方法,用于超声图像消噪. 通过减少斑点噪声,ADM-ExNet显著提高了图像质量,同时保留了关键的结构细节.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 超声波图像中的斑点噪声降低了图像质量,并阻碍了分析.
- 现有的否定方法往往会损害结构信息.
研究的目的:
- 为超声波图像开发一种基于扩散模型 (DM) 的新型消噪方法.
- 通过有效抑制噪音和保留结构细节来提高超声波图像质量.
主要方法:
- 开发了一个与生成对抗网络 (GAN) 结合的扩散模型 (DM),命名为ADM-ExNet.
- 该模型包含了用于生成器和区分器的U-Net结构,以及用于保留细节的特征提取网络.
- 在HC18,CAMUS和超声波神经数据集上进行了实验,模拟了高斯噪声.
主要成果:
- ADM-ExNet表现出显著的无噪声性能,PSNR改进超过12dB,MSE减少超过90%.
- 该方法实现了高结构相似度指数 (SSIM) 值,表明结构保存优越.
- 统计分析证实了ADM-ExNet对传统和深度学习方法的稳定性和优势.
结论:
- 拟议的ADM-ExNet有效地减少了超声波图像中的噪声,同时保留了必要的细节.
- 这种方法有望提高超声波成像的质量和诊断效用.
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