A novel complex-valued convolutional neural network for medical image denoising. (August 2021) - Rawat, Shubhankar; Rana, K.P.S.; Kumar, Vineet
Highlights: Complex-valued CNNs, CVMIDNet, for medical image denoising is implemented. CVMIDNet incorporated residual learning and was implemented on CXR image denoising. Performance of CVMIDNet is estimated for varying noise levels in CXR images. Additionally, CVMIDNet is also benchmarked against BM3D, DnCNN and RVMIDNet. CVMIDNet was found to be significantly superior to BM3D, DnCNN and RVMIDNet. Abstract: Several applications of complex-valued networks have been reported for computer vision tasks like image processing and classification. However, complex-valued convolutional neural networks are yet to be explored for medical image denoising. In this paper, a novel complex-valued convolutional neural network-based model, termed as CVMIDNet, is investigated for medical image denoising. Unlike traditional approaches, which learn clean images from noisy images, the proposed model utilizes residual learning, which learns noise from noisy images and then subtracts it from noisy images so as to obtain clean images. To assess the denoising performance of CVMIDNet, standard image quality metrics, namely, peak signal to noise ratio and the structural similarity index, have been used for five different additive white Gaussian noise levels in chest X-ray images. Chest X-ray denoising performance of CVMIDNet was compared with four recent state-of-the-art models, namely, Block-Matching and 3D (BM3D) filtering, DnCNN, Feature-guided Denoising Convolutional Neural Network (FDCNN), and deep CNN with residual learning. Additionally, it is also benchmarked against its real-valued counterpart, termed RVMIDNet. In all the reported performance investigations, CVMIDNet was found to be superior. For instance, for a Gaussian noise level of σ = 15, peak signal to noise ratio and structural similarity index values achieved by the CVMIDNet are 37.2010 and 0.9227, respectively, against the 36.2292 and 0.9086, 36.3203 and 0.9139, 35.0995 and 0.9005, 36.1830 and 0.8968, 34.2436 and 0.8874 achieved by BM3D filtering, DnCNN, RVMIDNet, FDCNN, and deep CNN with residual learning, respectively. Therefore, based on the presented investigations, it is concluded that CVMIDNet is a potential deep learning model for medical image denoising.