资源类型:
期刊
WOS体系:
Article
Pubmed体系:
Journal Article
收录情况:
◇ SCIE
文章类型:
论著
机构:
[1]Beijing Institute of Technology, Beijing, 100081, China.
[2]School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China.
[3]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
研究所
眼科研究所
首都医科大学附属北京同仁医院
首都医科大学附属同仁医院
ISSN:
0140-0118
关键词:
Retinal image enhancement
Contrast standardization
Blurriness grading
Adaptive enhancement
摘要:
Cataract affects the quality of fundus images, especially the contrast, due to lens opacity. In this paper, we propose a scheme to enhance different cataractous retinal images to the same contrast as normal images, which can automatically choose the suitable enhancement model based on cataract grading. A multi-level cataract dataset is constructed via the degradation model with quantified contrast. Then, an adaptive enhancement strategy is introduced to choose among three enhancement networks based on a blurriness classifier. The blurriness grading loss is proposed in the enhancement models to further constrain the contrast of the enhanced images. During test, the well-trained blurriness classifier can assist in the selection of enhancement networks with specific enhancement ability. Our method performs the best on the synthetic paired data on PSNR, SSIM, and FSIM and has the best PIQE and FID on 406 clinical fundus images. There is a 7.78% improvement for our method compared with the second on the introduced [Formula: see text] score without over-enhancement according to [Formula: see text], which demonstrates that the proper enhancement by our method is close to the high-quality images. The visual evaluation on multiple clinical datasets also shows the applicability of our method for different blurriness. The proposed method can benefit clinical diagnosis and improve the performance of computer-aided algorithms such as vessel tracking and vessel segmentation.© 2023. International Federation for Medical and Biological Engineering.
基金:
National Natural Science
Foundation of China (No. 82072007), the Key Scientific Research
Projects of Colleges and Universities in Henan Province (No. 23A520011),
and Key R&D and Promotion Projects of Henan Province (No.
232102211089).
WOS:
WOS:001083122300001
PubmedID:
37848753
中科院(CAS)分区:
出版当年[2023]版:
大类
|
4 区
医学
小类
|
2 区
数学与计算生物学
4 区
计算机:跨学科应用
4 区
工程:生物医学
4 区
医学:信息
最新[2023]版:
大类
|
4 区
医学
小类
|
2 区
数学与计算生物学
4 区
计算机:跨学科应用
4 区
工程:生物医学
4 区
医学:信息
JCR分区:
出版当年[2022]版:
Q2
MATHEMATICAL & COMPUTATIONAL BIOLOGY
Q3
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Q3
ENGINEERING, BIOMEDICAL
Q3
MEDICAL INFORMATICS
最新[2023]版:
Q2
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Q2
MATHEMATICAL & COMPUTATIONAL BIOLOGY
Q3
ENGINEERING, BIOMEDICAL
Q3
MEDICAL INFORMATICS
影响因子:
2.6
最新[2023版]
2.7
最新五年平均
3.2
出版当年[2022版]
3.1
出版当年五年平均
3.079
出版前一年[2021版]
2.6
出版后一年[2023版]
第一作者:
Yang Bingyu
第一作者机构:
[1]Beijing Institute of Technology, Beijing, 100081, China.
通讯作者:
Liu Hanruo
推荐引用方式(GB/T 7714):
Yang Bingyu,Cao Lvchen,Zhao He,et al.Adaptive enhancement of cataractous retinal images for contrast standardization[J].MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING.2024,62(2):357-369.doi:10.1007/s11517-023-02937-5.
APA:
Yang Bingyu,Cao Lvchen,Zhao He,Li Huiqi,Liu Hanruo&Wang Ningli.(2024).Adaptive enhancement of cataractous retinal images for contrast standardization.MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,62,(2)
MLA:
Yang Bingyu,et al."Adaptive enhancement of cataractous retinal images for contrast standardization".MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 62..2(2024):357-369