Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

Sundararaj Wilfred Franklin; Samuelnadar Edward Rajan

Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

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Author(s): Sundararaj Wilfred Franklin ¹ and Samuelnadar Edward Rajan ²
- Affiliations: 1: Department of Electronics and Communication Engineering, CSI Institute of Technology, Thovalai, Kanyakumari District, Nagercoil 629302, Tamilnadu, India;
  2: Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamilnadu, India
Source: Volume 8, Issue 10, October 2014, p. 601 – 609
DOI: 10.1049/iet-ipr.2013.0565 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 31/07/2013, Accepted 21/11/2013, Revised 18/10/2013, Published 29/05/2014

Diabetic retinopathy (DR) is a microvascular complication of long-term diabetes and it is the major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss because of DR is highly preventable with regular screening and timely intervention at the earlier stages. The presence of exudates is one of the primitive signs of DR and the detection of these exudates is the first step in automated screening for DR. Hence, exudates detection becomes a significant diagnostic task, in which digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the presence of exudates automatically and this helps the ophthalmologists in the diagnosis and follow-up of DR. Exudates are normally detected by their high grey-level variations and they have used an artificial neural network to perform this task by applying colour, size, shape and texture as the features. The performance of the authors algorithm has been prospectively tested by using DIARETDB1 database and evaluated by comparing the results with the ground-truth images annotated by expert ophthalmologists. They have obtained illustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based evaluation criterion and achieved a classification accuracy of 99.7%.

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Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

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