Artificial intelligence for chest X-ray image enhancement
The chest X-ray (CXR) imaging has been the most frequently performed radiographic examination for decades, and its demand continues to grow due to their critical role in diagnosing various diseases. However, the image quality of CXR has long been a factor limiting their diagnostic accuracy. As a pos...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Radiation Medicine and Protection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666555724001205 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856634813743104 |
|---|---|
| author | Liming Song Hongfei Sun Haonan Xiao Sai Kit Lam Yuefu Zhan Ge Ren Jing Cai |
| author_facet | Liming Song Hongfei Sun Haonan Xiao Sai Kit Lam Yuefu Zhan Ge Ren Jing Cai |
| author_sort | Liming Song |
| collection | DOAJ |
| description | The chest X-ray (CXR) imaging has been the most frequently performed radiographic examination for decades, and its demand continues to grow due to their critical role in diagnosing various diseases. However, the image quality of CXR has long been a factor limiting their diagnostic accuracy. As a post-processing procedure, image enhancement can cost-effectively improve image quality. Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. This review examines advancements in CXR image enhancement methods from 2018 to 2023, categorizing them into four groups: bone suppression, image denoising, super-resolution reconstruction, and contrast enhancement. For each group, the unique approaches, strengths, and challenges are analyzed. The review concludes by discussing shared challenges across these methods and proposing directions for future research. |
| format | Article |
| id | doaj-art-039278486f354afabab19679e5a9854e |
| institution | Kabale University |
| issn | 2666-5557 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Radiation Medicine and Protection |
| spelling | doaj-art-039278486f354afabab19679e5a9854e2025-02-12T05:32:54ZengElsevierRadiation Medicine and Protection2666-55572025-02-01616168Artificial intelligence for chest X-ray image enhancementLiming Song0Hongfei Sun1Haonan Xiao2Sai Kit Lam3Yuefu Zhan4Ge Ren5Jing Cai6Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Radiotherapy, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, ChinaDepartment of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, ChinaDepartment of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Radiology, Hainan Women and Children's Medical Center, Haikou 570206, China; Department of Radiology, Third People's Hospital of Longgang District, Shenzhen 518115, ChinaDepartment of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China; Corresponding author.Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong, China; Corresponding author. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.The chest X-ray (CXR) imaging has been the most frequently performed radiographic examination for decades, and its demand continues to grow due to their critical role in diagnosing various diseases. However, the image quality of CXR has long been a factor limiting their diagnostic accuracy. As a post-processing procedure, image enhancement can cost-effectively improve image quality. Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. This review examines advancements in CXR image enhancement methods from 2018 to 2023, categorizing them into four groups: bone suppression, image denoising, super-resolution reconstruction, and contrast enhancement. For each group, the unique approaches, strengths, and challenges are analyzed. The review concludes by discussing shared challenges across these methods and proposing directions for future research.http://www.sciencedirect.com/science/article/pii/S2666555724001205Chest X-raysImage enhancementDeep learningBone suppressionContrast enhancement |
| spellingShingle | Liming Song Hongfei Sun Haonan Xiao Sai Kit Lam Yuefu Zhan Ge Ren Jing Cai Artificial intelligence for chest X-ray image enhancement Radiation Medicine and Protection Chest X-rays Image enhancement Deep learning Bone suppression Contrast enhancement |
| title | Artificial intelligence for chest X-ray image enhancement |
| title_full | Artificial intelligence for chest X-ray image enhancement |
| title_fullStr | Artificial intelligence for chest X-ray image enhancement |
| title_full_unstemmed | Artificial intelligence for chest X-ray image enhancement |
| title_short | Artificial intelligence for chest X-ray image enhancement |
| title_sort | artificial intelligence for chest x ray image enhancement |
| topic | Chest X-rays Image enhancement Deep learning Bone suppression Contrast enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S2666555724001205 |
| work_keys_str_mv | AT limingsong artificialintelligenceforchestxrayimageenhancement AT hongfeisun artificialintelligenceforchestxrayimageenhancement AT haonanxiao artificialintelligenceforchestxrayimageenhancement AT saikitlam artificialintelligenceforchestxrayimageenhancement AT yuefuzhan artificialintelligenceforchestxrayimageenhancement AT geren artificialintelligenceforchestxrayimageenhancement AT jingcai artificialintelligenceforchestxrayimageenhancement |