| 李云鹤*,陈伦强*,赵慧岩**,吴绍华***.基于生成对抗网络的CT图像无监督超分辨率分析[J].高技术通讯(中文),2023,33(7):704~712 |
| 基于生成对抗网络的CT图像无监督超分辨率分析 |
| Unsupervised super resolution analysis of CT images based on generative adversarial networks |
| |
| DOI：10. 3772/ j. issn. 1002-0470. 2023. 07. 004 |
| 中文关键词: 超分辨率分析； 计算机断层成像（CT）； 生成对抗网络（GAN）； 深度学习 |
| 英文关键词: super-resolution, computed tomography (CT), generative adversarial network (GAN), deep learning |
| 基金项目: |
| 作者 | 单位 | | 李云鹤* | (*肇庆学院电子与电气工程学院肇庆 526061)
(**东北石油大学电气信息工程学院大庆 163319)
(***哈尔滨工业大学（深圳）电子与信息工程学院深圳 518055) | | 陈伦强* | | | 赵慧岩** | | | 吴绍华*** | |
|
| 摘要点击次数: 729 |
| 全文下载次数: 834 |
| 中文摘要: |
| 提高计算机断层成像(CT)医疗影像的分辨率有助于医生更精确地识别病变部位，具有重要临床诊断意义。本文研究在没有高 低分辨率图像对数据的条件下，使用仅包含低分辨率图像的数据集，通过降质网络和注入噪声获得与真实图像同域的低分辨率图像，进而构造接近天然图像对的训练数据集。并且设计了包括超分辨生成器、超分辨鉴别器和超分辨特征提取器的超分辨率生成对抗网络（DeSRGAN），实现对CT影像4倍超分辨率分析。实验测试表明，超分辨率分析生成的4倍CT图像在NIQE、BRISQUE和PIQE等无参考图像质量评估指标的定量对比中，DeSRGAN方法均优于最新的单图像超分辨率的增强型深度残差网络（EDSR）、残差信道注意力网络（RCAN）、增强型超分辨率生成对抗性网络（ESRGAN）等方法生成的图像。同时在直观视觉效果上，DeSRGAN方法生成的图像具有更清晰细节和更好感知效果。 |
| 英文摘要: |
| Improving the resolution of computed tomography (CT) medical images can help doctors to identify the lesion more accurately, which has important clinical significance. This paper studies how to obtain a low resolution image in the same domain as the real image by using a dataset containing only low resolution images without high-low resolution image pair data, and then constructs a training dataset close to the natural image pair by degrading the network and injecting noise. And a super-resolution generative adversarial networks including a super-resolution generator, a super-resolution discriminator and a super-resolution feature extractor (DeSRGAN) is designed to achieve X4 times super-resolution analysis of CT images. Experimental tests show that the DeSRGAN method is superior to the latest EDSR, RCAN, ESRGAN and other methods in quantitative comparison of X4 times CT images generated by super-resolution analysis without reference image quality evaluation indicators such as NIQE, BRISQUE and PIQE. At the same time, in terms of intuitive visual effects, the images generated by DeSRGAN method have clearer details and better perceptual effects. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
