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Buletin Teknik Elektro dan Informatika
Article . 2016 . Peer-reviewed
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Buletin Teknik Elektro dan Informatika
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Buletin Teknik Elektro dan Informatika
Article . 2016 . Peer-reviewed
Data sources: Crossref
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Buletin Teknik Elektro dan Informatika
Article
License: CC BY
Data sources: UnpayWall
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Bright Lesion Detection in Color Fundus Images Based on Texture Features

Authors: Ratna Bhargavi V; Ranjan K. Senapati;

Bright Lesion Detection in Color Fundus Images Based on Texture Features

Abstract

In this paper a computer aided screening system for the detection of bright lesions or exudates using color fundus images is proposed. The proposed screening system is used to identify the suspicious regions for bright lesions. A texture feature extraction method is also demonstrated to describe the characteristics of region of interest. In final stage the normal and abnormal images are classified using Support vector machine classifier. Our proposed system obtained the effective detection performance compared to some of the state–of–art methods.

Keywords

Segmentation, Diabetic retinopathy, Feature extraction, Computer aided screening, Classification

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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