Automatic non-proliferative diabetic retinopathy screening system based on color fundus image

被引:19
|
作者
Xiao, Zhitao [1 ,2 ]
Zhang, Xinpeng [1 ,2 ]
Geng, Lei [1 ,2 ]
Zhang, Fang [1 ,2 ]
Wu, Jun [1 ,2 ]
Tong, Jun [3 ]
Ogunbona, Philip O. [3 ]
Shan, Chunyan [4 ]
机构
[1] Tianjin Polytech Univ, Sch Elect & Informat Engn, 399 Binshui West Rd, Tianjin 300387, Peoples R China
[2] Tianjin Key Lab Optoelect Detect Technol & Syst, Tianjin 300387, Peoples R China
[3] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong, NSW 2522, Australia
[4] Tianjin Med Univ, Metab Dis Hosp, Tianjin 300070, Peoples R China
基金
高等学校博士学科点专项科研基金;
关键词
Automatic screening system; Color fundus image; Early lesions; Non-proliferative diabetic retinopathy; RED LESIONS; SEGMENTATION; LOCALIZATION; VESSELS;
D O I
10.1186/s12938-017-0414-z
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. Methods: This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. Results: The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Conclusions: Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.
引用
收藏
页数:19
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