Cluster Based Method to Spot Hard Exudates in Moderate Stage of Non-Proliferative Diabetic Retinopathy

Authors(2) :-K. Saraswathi, Dr. V. Ganesh Babu

Diabetic retinopathy (DR) is a diabetes related eye disease which occurs when blood vessels in the retina become swelled and leaks fluid which ultimately leads to vision loss. Several image processing techniques including Image Enhancement, Segmentation, Image Fusion, Morphology, Classification, and registration has been developed for the early detection of DR on the basis of features such as blood vessels, exudes, hemorrhages, and microaneurysms. The damage caused by diabetic retinopathy can be prevented by the early detection of microaneurysms, exudates and hemorrhages in the retina. Hard Exudates are medical sign of DR in fundus image [1]. Presence of exudates described the levels of DR. Timely recognition of exudates can reduce the risks of loss of sight. The proposed paper has discussed about the identification of exudates using K-means clustering in digital image of fundus using different preprocessing and feature extraction techniques.

Authors and Affiliations

K. Saraswathi
Research Scholar, Bharathiar University, Assistant Professor in Computer Science, Nehru Memorial College, Puthanampatti, Trichy, India
Dr. V. Ganesh Babu
Assistant Professor, Department of Computer Science, Government College for Women, Maddur, Mandya, Karnataka, India

Diabetic Retinopathy, Hard Exudates, Fundus Image, K-means.

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 564-566
Manuscript Number : CSEIT1725120
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Saraswathi, Dr. V. Ganesh Babu, "Cluster Based Method to Spot Hard Exudates in Moderate Stage of Non-Proliferative Diabetic Retinopathy ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.564-566, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725120

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