Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Imagesopen access
- Authors
- Lim, Sewon; Nam, Hayun; Shin, Hyemin; Jeong, Sein; Kim, Kyuseok; Lee, Youngjin
- Issue Date
- Dec-2023
- Publisher
- MDPI
- Keywords
- breast X-ray image; generative adversarial network; noise reduction; quantitative evaluation of image quality; virtual grid
- Citation
- Journal of Imaging, v.9, no.12
- Journal Title
- Journal of Imaging
- Volume
- 9
- Number
- 12
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89928
- DOI
- 10.3390/jimaging9120272
- ISSN
- 2313-433X
2313-433X
- Abstract
- In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images. © 2023 by the authors.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89928)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.