کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4957387 | 1445076 | 2017 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automatic diabetic retinopathy diagnosis using adjustable ophthalmoscope and multi-scale line operator
ترجمه فارسی عنوان
تشخیص رتینوپاتی دیابتی اتوماتیک با استفاده از چشم افترشی قابل تنظیم و اپراتور خط چند منظوره
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
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%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pervasive and Mobile Computing - Volume 41, October 2017, Pages 490-503
Journal: Pervasive and Mobile Computing - Volume 41, October 2017, Pages 490-503
نویسندگان
Meng Qu, Chun Ni, Mufan Chen, Linghan Zheng, Ling Dai, Bin Sheng, Ping Li, Qiang Wu,