Title :
Diagnosis of diabetic retinopathy using morphological process and SVM classifier
Author :
Gandhi, Mallika ; Dhanasekaran, R.
Author_Institution :
Dept. of Electron. & Commun. Eng., Syed Ammal Eng. Coll., Ramanathapuram, India
Abstract :
Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.
Keywords :
biomedical optical imaging; blood; blood vessels; decision making; diseases; haemodynamics; image classification; image colour analysis; medical image processing; sugar; support vector machines; vision; SVM classifier; blood vessels; circulatory system; colour fundus retinal images; decision making; diabetic patients; diabetic retinopathy diagnosis; eye disease; filtering; glucose level; inflammation; morphological process; ophthalmologists; retinal fundus diseases; vision loss; Biomedical imaging; Diabetes; Diseases; Optical filters; Optical imaging; Retina; Retinopathy; Diabetic Retinopathy; Erosion; Exudates; Support Vector Machine Classifier;
Conference_Titel :
Communications and Signal Processing (ICCSP), 2013 International Conference on
Conference_Location :
Melmaruvathur
Print_ISBN :
978-1-4673-4865-2
DOI :
10.1109/iccsp.2013.6577181