IMAGE SEGMENTATION IDENTIFICATION FOR EXUDATE DETECTION USING DIABETIC RETINOPATHY
Author’s Name : Reshma Cr | S Nikilla
Volume 05 Issue 04 Year 2018 ISSN No: 2349-252X Page no: 17-20
From the observation of past 10 years medical report, about 30% of patients with diabetic acquired great hazard of eye disease. Severe stage of diabetics may cause damage in retina blood vessels. The affected blood vessel further may produce loss of blood and another lymphatic. The swelling on retinal tissue leads to Diabetic Retinopathy (DR). Serious stage of DR leads to blindness. Early detection is essential for the prevention of blindness. Automatic detection is carried by various techniques such as pre-processing, feature extraction, exudates segmentation and classification of exudates are performed using different approaches. This paper describes various method and algorithm implemented for the early detection of Diabetic Retinopathy (DR).
Diabetic Retinopathy (DR), Hard Exudates, Fundus Images, Support Vector Machine (SVM)
- Priyadarshini Patil, Pooja Shettar, Prashant Narayankar,Mayur Patil ,”An Efficient Method of Detecting Exudates in Diabetic Retinopathy : Using Texture Edge Features” , 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
- Lili Xu, Shuqian Luo,” Support Vector Machine Based Method For Identifying Hard Exudates In Retinal Images ” , 978-1-4244- 5076-3/09/$26.00 ©2009 IEEE.
- Sudeshna Sil Kar and Santi P. Maity , ” Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy”, This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME.2017.2707578, IEEE Transactions on Biomedical Engineering.
- Bouacheria Mohamed Cherfa Yazid BelkhamsaNourreddine, BenouadahAbdelmalekCherfaAssia , ” Non- Proliferative Diabetic Retinopathy Detection Using Mathematical Morphology”, 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME).
- AmbajiS . Jadhav, Pushpa B. Patil, ” Detection of Exudates for Diabetic Retinopathy using Wavelet Transform”, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017).
- Santhakumar R, MeghaTandur, , E R Rajkumar ,” Machine Learning Algorithm for Retinal Image Analysis”, 978-1-5090- 2597-8/16/$31.00_c 2016 IEEE.
- P. R. Asha, S. Karpagavalli ,” Diabetic Retinal Exudates Detection using Machine Learning Techniques”, 2015 International Conference on Advanced Computing and Communication Systems (ICACCS -2015), Jan. 05 – 07, 2015, Coimbatore, INDIA.
- Shuang Yu, Di Xiao and Yogesan Kanagasingam , “Exudate Detection for Diabetic Retinopathy With Convolutional Neural Networks”, 978-1-5090-2809- 2/17/$31.00 ©2017 IEEE.
- Mohamed Omar, Fouad Khelifi and Muhammad Atif Tahir, ” Detection and Classification of Retinal Fundus Images Exudates using Region based Multiscale LBP Texture Approach”, CoDIT’16 – April 6-8, 2016, Malta.
- T.Ruba, K.Ramalakshmi ,” Identification and segmentation of exudates using SVM classifier”, IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15.