Identification and Classification of Retinal Lesions for Early Detection of Diabetic Retinopathy using Fundal Image

Author(s):  
N Chethan ◽  
KC R Nisha
2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.


2014 ◽  
Vol 45 ◽  
pp. 161-171 ◽  
Author(s):  
M. Usman Akram ◽  
Shehzad Khalid ◽  
Anam Tariq ◽  
Shoab A. Khan ◽  
Farooque Azam

2013 ◽  
Vol 46 (1) ◽  
pp. 107-116 ◽  
Author(s):  
M. Usman Akram ◽  
Shehzad Khalid ◽  
Shoab A. Khan

2020 ◽  
Author(s):  
Dr. Vrinda Shiva Shetty ◽  
Harshitha M ◽  
Nitika Choudhary ◽  
Namaratha Karanth ◽  
Akshatha S B
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