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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence
Published by : Advanced Scientific Research
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0975-2366
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IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

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Block Based Probability Intensity Feature Extraction for Automatic Glaucoma Detection

Author: M. ARULMARY, DR. S.P. VICTOR
Abstract: Automatic glaucoma detection is one of the interesting areas for more researchers. There are several researches based on Cup to Disc Ratio. The objective of this paper is to identify glaucoma suspect eyes with high accuracy. In this paper, features are extracted by dividing the fundus images into blocks and identifying the high intensity value in each block. The glaucoma suspect images have larger number of blocks with high intensity whereas the normal eyes have lesser. The features are trained and classified using Support Vector Machine (SVM) classifier. The proposed method is tested in RIM-One r3 database. The experimental results substantially showed that the proposed method achieves 92% specificity at 86% sensitivity with 0.865 AUC.
Keyword: SVM classifier, Cup to Disc Ratio, Intra Ocular Pressure, ELM classifier
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