Affiliations: [a]
School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China
| [b]
Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
Correspondence:
[*]
Corresponding authors. Yonggong Ren, School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning 116082, China. E-mail: [email protected]. and Dang N.H. Thanh, Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam. E-mail: [email protected].
Abstract: BACKGROUND:In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution. OBJECTIVE:In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet). METHODS:Firstly, the output results of L0 Gradient Minimization are used as the labels of a dental CT image dataset to form a pseudo-image pair with the real dental CT images, which are used to train the noise generation network to estimate real noise distribution. Then, for the lung CT images of the LIDC/IDRI database, we migrate the real noise to the noise-free lung CT images, to construct a new almost-real noisy images dataset. Since dental images and lung images are all CT images, this migration can be achieved. The denoising network is trained to realize the denoising of real LDCT for dental images by using this dataset but can extend for any low-dose CT images. RESULTS:To prove the effectiveness of our NGRNet, we conduct experiments on lung CT images with synthetic noise and tooth CT images with real noise. For synthetic noise image datasets, experimental results show that NGRNet is superior to existing denoising methods in terms of visual effect and exceeds 0.13dB in the peak signal-to-noise ratio (PSNR). For real noisy image datasets, the proposed method can achieve the best visual denoising effect. CONCLUSIONS:The proposed method can retain more details and achieve impressive denoising performance.
Keywords: Low-dose CT images, image denoising, noise generation networks, real noise, deep learning
