Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images
DC Field | Value | Language |
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dc.contributor.author | Lim, Sewon | - |
dc.contributor.author | Nam, Hayun | - |
dc.contributor.author | Shin, Hyemin | - |
dc.contributor.author | Jeong, Sein | - |
dc.contributor.author | Kim, Kyuseok | - |
dc.contributor.author | Lee, Youngjin | - |
dc.date.accessioned | 2024-01-02T11:30:17Z | - |
dc.date.available | 2024-01-02T11:30:17Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.issn | 2313-433X | - |
dc.identifier.issn | 2313-433X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89928 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images | - |
dc.type | Article | - |
dc.identifier.wosid | 001130518800001 | - |
dc.identifier.doi | 10.3390/jimaging9120272 | - |
dc.identifier.bibliographicCitation | Journal of Imaging, v.9, no.12 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85180463330 | - |
dc.citation.title | Journal of Imaging | - |
dc.citation.volume | 9 | - |
dc.citation.number | 12 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | breast X-ray image | - |
dc.subject.keywordAuthor | generative adversarial network | - |
dc.subject.keywordAuthor | noise reduction | - |
dc.subject.keywordAuthor | quantitative evaluation of image quality | - |
dc.subject.keywordAuthor | virtual grid | - |
dc.subject.keywordPlus | BEDSIDE CHEST RADIOGRAPHY | - |
dc.subject.keywordPlus | SCATTER CORRECTION | - |
dc.subject.keywordPlus | SOFTWARE | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
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