Low dose CT image enhancement based on generative adversarial network

doi:10.11772/j.issn.1001-9081.2021101710

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (1): 280-288.DOI: 10.11772/j.issn.1001-9081.2021101710

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Low dose CT image enhancement based on generative adversarial network

HU Ziqi^1,2,3, XIE Kai^1,2,3, WEN Chang^3,4, LI Meiran¹, HE Jianbiao⁵

1.School of Electronic Information， Yangtze University， Jingzhou Hubei 434023， China
2.National Demonstration Center for Experimental Electrical and Electronic Education，Yangtze University， Jingzhou Hubei 434023， China
3.Western Research Institute， Yangtze University， Karamay Xinjiang 834099， China
4.School of Computer Science， Yangtze University， Jingzhou Hubei 434023， China
5.School of Computer Science and Engineering， Central South University， Changsha Hunan 410083， China

Received:2021-10-08 Revised:2022-01-13 Online:2023-01-12
Contact: XIE Kai， born in 1974， Ph. D.， professor. His research interests include signal and information processing， image processing， artificial intelligence.
About author:HU Ziqi， born in 1997， M. S. candidate. His research interests include image processing， artificial intelligence；WEN Chang， born in 1979， M. S.， lecturer. Her research interests include image processing， artificial intelligence；LI Meiran， born in 2001. Her research interests include signal and image processing；HE Jianbiao， born in 1964， M. S.， associate professor. His research interests include signal and information processing；
Supported by:
This work is partially supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region （2020D01A131）， Program of Department of Education of Hubei Province （B2019039）， Yangtze University Training Program for Undergraduate Innovation and Entrepreneurship （Yz2020059）.

生成对抗网络下的低剂量CT图像增强

胡紫琪^1,2,3, 谢凯^1,2,3, 文畅^3,4, 李美然¹, 贺建飚⁵

1.长江大学电子信息学院,湖北荆州 434023
2.长江大学电工电子国家级实验教学示范中心,湖北荆州 434023
3.长江大学西部研究院,新疆克拉玛依 834099
4.长江大学计算机科学学院,湖北荆州 434023
5.中南大学计算机学院,长沙410083

作者简介:胡紫琪（1997—），男，湖北黄冈人，硕士研究生，主要研究方向：图像处理、人工智能；谢凯（1974—），男，湖北荆州人，教授，博士，主要研究方向：信号与信息处理、图像处理、人工智能 email：pami2009@163.com；文畅（1979—），女，湖北荆州人，讲师，硕士，主要研究方向：图像处理、人工智能；李美然（2001—），女，河北石家庄人，主要研究方向：信号与图像处理；贺建飚（1964—），男，湖南长沙人，副教授，硕士，主要研究方向：信号与信息处理；
基金资助:
新疆维吾尔自治区自然科学基金资助项目（2020D01A131）；湖北省教育厅项目（B2019039）；长江大学大学生创新创业训练计划项目（Yz2020059）。

Abstract

Abstract: In order to remove the noise in Low Dose Computed Tomography （LDCT） images and enhance the display effect of the denoised images， an LDCT image enhancement algorithm based on Generative Adversarial Network （GAN） was proposed. Firstly， GAN was combined with perceptual loss and structure loss to denoise the LDCT image. Then， dynamic gray?scale enhancement and edge contour enhancement were performed to the denoised image respectively. Finally， Non?Subsampled Contourlet Transform （NSCT） was used to decompose the enhanced image into multi?directional coefficient sub?images in the frequency domain， and the paired high? and low?frequency sub?images were adaptively fused with Convolutional Neural Network （CNN） to reconstruct the enhanced Computed Tomography （CT） image. Using the real clinical data of the AAPM competition as the experimental dataset， the image denoising， enhancement， and fusion experiments were carried out. The results of the proposed method are 33.015 5 dB， 0.918 5， and 5.99 on Peak Signal?to?Noise Ratio （PSNR）， Structural Similarity Index Measure （SSIM）， and Root Mean Square Error （RMSE） respectively. Experimental results show that the proposed algorithm retains the detailed information of the CT image while removing noise， and improves the brightness and contrast of the image， which helps doctors analyze the patient’s condition more accurately.

Key words: Low Dose Computed Tomography (LDCT), medical image denoising, Generative Adversarial Network (GAN), medical image enhancement, Non?Subsampled Contourlet Transform (NSCT), image fusion

摘要： 为去除低剂量计算机断层扫描（LDCT）图像中的噪声，增强去噪后图像的显示效果，提出一种生成对抗网络（GAN）下的LDCT图像增强算法。首先，将GAN与感知损失、结构损失相结合对LDCT图像进行去噪；然后，对去噪后的图像分别进行动态灰度增强和边缘轮廓增强；最后，利用非下采样轮廓波变换（NSCT）将增强后的图像在频域上分解为具有多方向性的系数子图，并将配对的高低频子图使用卷积神经网络（CNN）进行自适应融合，以重构得到增强后的计算机断层扫描（CT）图像。使用AAPM比赛公开的真实临床数据作为实验数据集，进行图像去噪、增强、融合实验，所提方法在峰值信噪比（PSNR）、结构相似度（SSIM）和均方根误差（RMSE）上的结果分别为33.015 5 dB、0.918 5和5.99。实验结果表明，所提算法在去除噪声的同时能保留CT图像的细节信息，提高图像的亮度和对比度，有助于医生更加准确地分析病情。

关键词: 低剂量计算机断层扫描, 医学图像去噪, 生成对抗网络, 医学图像增强, 非下采样轮廓波变换, 图像融合

CLC Number:

TP391.41

HU Ziqi, XIE Kai, WEN Chang, LI Meiran, HE Jianbiao. Low dose CT image enhancement based on generative adversarial network[J]. Journal of Computer Applications, 2023, 43(1): 280-288.

胡紫琪, 谢凯, 文畅, 李美然, 贺建飚. 生成对抗网络下的低剂量CT图像增强[J]. 《计算机应用》唯一官方网站, 2023, 43(1): 280-288.

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Low dose CT image enhancement based on generative adversarial network

生成对抗网络下的低剂量CT图像增强

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