Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 15; p. 3348
• Main Authors: Chi, Jianning, Zhang, Yifei, Yu, Xiaosheng, Wang, Ying, Wu, Chengdong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.07.2019; MDPI
• Subjects: Algorithms; deep convolutional neural network; Deep learning; dense connection; inception structure; Information processing; International conferences; Mean square errors; medical image enhancement; Methods; Neural networks; Noise; Pattern recognition; Principal components analysis; Quality; Quality control; residual network; Tomography; United States > US; China; inception structure; dense connection; medical image enhancement; residual network; deep convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19153348

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.