A computational modeling for the detection of diabetic retinopathy severity 



Pavan Kumar Mishra1, Abhijit Sinha1, Kaveti Ravi Teja1, Nitin Bhojwani1, Sagar Sahu1 & Awanish Kumar2*



1Department of Information Technology, National Institute of Technology, Raipur, Chhattisgarh, India; 2Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, India


Email; *Corresponding author


Article Type




Received June 17, 2014; Revised August 06, 2014; Accepted August 07, 2014; Published September 30, 2014



Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.



Diabetic retinopathy, fundus, blood vessel, optic disc, exudates, image processing.



Mishra et al.   Bioinformation 10(9): 556-561 (2014)

Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.