A Machine Vision Based Detection & Classification of Neovascularisation in Retinal Blood Vessels

Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Prol...

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Vydané v:International journal of innovative technology and exploring engineering Ročník 8; číslo 10; s. 1468 - 1471
Hlavní autori: Sudha, S., Srinivasan, A., Karthik, S., Krishnan, S.Hari, Prakash, S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 30.08.2019
ISSN:2278-3075, 2278-3075
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Abstract Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. In this proposed system techniques are introduced to detect and classify neovascularisation. Input fundus image is preprocessed by median filtering and further new vessels are segmented by using Fuzzy c-means clustering algorithm. After segmentation SIFT features are extracted and are used to train support vector machine (SVM) classifier. This automated system has been tested for 70 fundus images and accuracy of 96% is achieved.
AbstractList Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. In this proposed system techniques are introduced to detect and classify neovascularisation. Input fundus image is preprocessed by median filtering and further new vessels are segmented by using Fuzzy c-means clustering algorithm. After segmentation SIFT features are extracted and are used to train support vector machine (SVM) classifier. This automated system has been tested for 70 fundus images and accuracy of 96% is achieved.
Author Karthik, S.
Krishnan, S.Hari
Srinivasan, A.
Sudha, S.
Prakash, S.
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CorporateAuthor Department of ECE School of EEE, SRC SASTRA Deemed University, Kumbakonam, Tamilnadu, India
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