Abstract
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.
Highlights
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)
The digital image quality generated from recent imaging devices is always interrupted by noise, storage, and transmission loss [2], resulting in noisy low-resolution images that may degrade the effects of the subsequent steps of computer assisted diagnosis system (CADs), including segmentation, feature extraction, and diagnosis
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 so thatand theblurring structure featuresMoreover, of the image are transferred thereconstruction network moreisthan the noise features
Summary
The digital image quality generated from recent imaging devices is always interrupted by noise, storage, and transmission loss [2], resulting in noisy low-resolution images that may degrade the effects of the subsequent steps of CADs, including segmentation, feature extraction, and diagnosis. Given the fact that the degradation process of an image can be summarized as blurring [3], down-sampling [4], and noise interference [5], traditional image quality enhancement methods typically include two steps: Image de-noising was performed first and the image super-resolution was implemented subsequently. Image de-noising methods can be categorized into two types: (1) Prior model-based algorithms and (2) deep learning-based algorithms. Some representative model-based de-noising methods include the non-local means algorithm (NLM) [6], singular value decomposition algorithm (K-SVD) [7], and block-matching and 3D filtering (BM3D) [8]
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