Title
Automatic diabetic retinopathy diagnosis using adjustable ophthalmoscope and multi-scale line operator.
Abstract
Diabetic Retinopathy (DR), the most common one of diabetic eye diseases that cause loss of vision and blindness, has become one of major health problems today. However, DR can be eased through timely treatment and periodical screening. In this paper, we proposes an automatic diabetic retinopathy diagnostic system to help patients know about their retinal conditions. We design a portable ophthalmoscope, which is composed of a retinal lens, a smartphone and a frame between them to help patients take fundus images anywhere and anytime. Then the images are transmitted to be analyzed, including localization of optic disk and macular, vessel segmentation, detection of lesions, and grading of DR. We use a multi-scale line operator to improve accuracy in segmenting small-scale vessels, a binary mask and image restoration to reduce the effect of the existence of the vessels on optic disk localization. After the analysis, the fundus image are then graded as normal, mild Non-Proliferative Diabetic Retinopathy (NPDR), moderate NPDR or severe NPDR. The grading process uses region segmentation to improve the efficiency. The final grading results are tested based on the fundus images provided by the hospitals. We evaluate our system through comparing our grading result with those graded by experts, which comes out with an overall accuracy of up to 85%.
Year
DOI
Venue
2017
10.1016/j.pmcj.2017.04.003
Pervasive and Mobile Computing
Keywords
Field
DocType
Diabetic retinopathy screening,Ophthalmoscope,DR grading,Multi-scale line operator
Computer science,Computer network,Artificial intelligence,Optometry,Image restoration,Diabetic retinopathy,Vessel segmentation,Computer vision,Diagnostic system,Segmentation,Fundus (eye),Optic disk,Blindness
Journal
Volume
Issue
ISSN
41
C
1574-1192
Citations 
PageRank 
References 
1
0.35
24
Authors
8
Name
Order
Citations
PageRank
Meng Qu110.35
Chun Ni210.35
Mufan Chen310.69
Linghan Zheng410.35
Ling Dai5152.74
Bin Sheng636861.19
Ping Li720240.76
Qiang Wu8132.91