Authors :
S. Sumithra; K. R. Remya; Dr. M. N. Giri Prasad
Volume/Issue :
Volume 5 - 2020, Issue 9 - September
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3i2XIfF
DOI :
10.38124/IJISRT20SEP342
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Diabetic retinopathy is an eye disease and
causes vision loss to the people who are suffering longer
from the diabetes. Exudates, bright and red lesions are
identified in the diabetic retinal eye. Automatic
detection and localization of macular edema is a
challenging issue since exudates have non uniform
illumination and are low contrasted. Proposed
algorithm to detect macular edema encompasses Simple
Linear Iterative Clustering, Fisher linear discriminant
and Support vector machine classifer. Optic Disc
extraction prior to exudates extraction is also
introduced. Performance of the proposed detection
algorithm is tested on easily available databases:
Diaretdb1, Messidor and E_optha Ex. Proposed method
shows an accuracy of 97.81%, specificity 98.65 and
Sensitivity 82.71%.
Keywords :
Macular Edema, Exudates, Superpixel, Simple Linear Iterative Clustering, Fisher’s Linear Discriminant, Support Vector Machine.
Diabetic retinopathy is an eye disease and
causes vision loss to the people who are suffering longer
from the diabetes. Exudates, bright and red lesions are
identified in the diabetic retinal eye. Automatic
detection and localization of macular edema is a
challenging issue since exudates have non uniform
illumination and are low contrasted. Proposed
algorithm to detect macular edema encompasses Simple
Linear Iterative Clustering, Fisher linear discriminant
and Support vector machine classifer. Optic Disc
extraction prior to exudates extraction is also
introduced. Performance of the proposed detection
algorithm is tested on easily available databases:
Diaretdb1, Messidor and E_optha Ex. Proposed method
shows an accuracy of 97.81%, specificity 98.65 and
Sensitivity 82.71%.
Keywords :
Macular Edema, Exudates, Superpixel, Simple Linear Iterative Clustering, Fisher’s Linear Discriminant, Support Vector Machine.
