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<records>

  <record>
    <language>eng</language>
          <publisher>Oriental Scientific Publishing Company</publisher>
        <journalTitle>Biomedical and Pharmacology Journal</journalTitle>
          <issn>0974-6242</issn>
            <publicationDate>2017-03-25</publicationDate>
    
        <volume>10</volume>
        <issue>1</issue>

 
    <startPage>459</startPage>
    <endPage>465</endPage>

	 
      <doi>10.13005/bpj/1130</doi>
        <publisherRecordId>13333</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">An Efficient Detection System for Screening Glaucoma in Retinal Images</title>

    <authors>
	 


      <author>
       <name>N. B. Prakash</name>

 
		
	<affiliationId>1</affiliationId>
      </author>
    

	 


      <author>
       <name>D. Selvathi</name>


		
	<affiliationId>2</affiliationId>

      </author>
    

	

	


	


	
    </authors>
    
	    <affiliationsList>
	    
		
		<affiliationName affiliationId="1">Department of Electrical and Electronics Engineering National Engineering College, India. </affiliationName>
    

		
		<affiliationName affiliationId="2">Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, India.</affiliationName>
    
		
		
		
		
	  </affiliationsList>






    <abstract language="eng">Glaucoma is the retinal disorder which leads to irreversible vision loss. The main root cause of this disorder is hyper tension, which affects the optic nerves in retinal image. In this paper, an efficient diagnosis system is proposed for screening the Glaucoma disorder using retinal images of the patients. The Optic Disc (OD) and Optic Cup (OC) are segmented from retinal image and further Neuro retinal rim region is detected. The features such as Effective Local Binary Pattern (ELBP), Grey Level Coocuurence Matrix (GLCM) and Optic Band features are extracted from this neuro retinal rim region. These features are trained and classified using Support Vector Machine (SVM) classifier.</abstract>

    <fullTextUrl format="html">https://biomedpharmajournal.org/vol10no1/an-efficient-detection-system-for-screening-glaucoma-in-retinal-images/</fullTextUrl>

<keywords language="eng">

      
        <keyword>Glaucoma</keyword>
      

      
        <keyword> Optic Disc</keyword>
      

      
        <keyword> Optic Cup</keyword>
      

      
        <keyword> features</keyword>
      

      
        <keyword> retinal images</keyword>
      
</keywords>
  </record>
</records>