Open Access

ARTICLE

Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

Anoud A. Mahmoud^1,*, Hanaa A. Sayed^2,3, Sara S. Mohamed¹

1 Department of Mathematics and Computer Science, Faculty of Science, New Valley University, New Valley, 72511, Egypt
2 Department of Computer Science, College of Computer Science and Engineering Taibah University, Medina, 42221, KSA
3 Department of Mathematics, Faculty of Science, Assiut University, Assiut, 71516, Egypt

* Corresponding Author: Anoud A. Mahmoud. Email:

Computers, Materials & Continua 2023, 75(2), 4535-4552. https://doi.org/10.32604/cmc.2023.037087

Received 22 October 2022; Accepted 13 February 2023; Issue published 31 March 2023

Abstract

Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network (WGAN). They were incorporated with different loss functions, including Visual Geometry Group (VGG), Structural Similarity Loss (SSIM), and Structurally Sensitive Loss (SSL), to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions. Furthermore, experiments have been conducted on the Graphical Processing Unit (GPU) and Central Processing Unit (CPU) to compare the performance of the proposed models. The results demonstrated that images from the proposed WGAN-SSIM, WGAN-VGG-SSIM, and WGAN-VGG-SSL were denoised better than those from state-of-the-art models (WGAN, WGAN-VGG, and SMGAN) and converged to a stable equilibrium compared with WGAN and WGAN-VGG. The proposed models are effective in reducing noise, suppressing artifacts, and maintaining informative structure and texture details, especially WGAN-VGG-SSL which achieved a high peak-signal-to-noise ratio (PNSR) on both GPU (26.1336) and CPU (25.8270). The average accuracy of WGAN-VGG-SSL outperformed that of the state-of-the-art methods by 1 percent. Experiments prove that the WGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

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