A Proposed Segmentation and Classification Algorithm of Diabetic Retinopathy Images for Exudates Disease

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The retinal image diagnosis is a significant methodology for diabetic retinopathy analysis. The diabetic retinopathy is a one of the major problematic diseases that provides changes in the blood vessels of the retinal that may issue blindness if it is not properly prevented and should be treated at the early stage. The Principle Component Analysis (PCA) algorithm is proposed to improve the contrast and brightness of the image. This paper presents the novel algorithm for blood vessel segmentation using unsupervised algorithm.  The normalized graph cut segmentation with Curvelet transform is applied to segment the blood vessel to determine the thickness of the blood vessel and it is considered as one of the key feature to classify the diabetic retinopathy. The multi-resolution curvelet transform is used to improve the blood vessel segmentation. The PCA algorithm is used to provide the gradient of the image for accurate segmentation of blood vessel. Optic disc is an important key feature of retinal image that is first process for analysis behavior of disease identification. The optic disc is removed by applying morphological erosion and dilation operation. The proposed localization method consists of Hough transform to detect the circular and elliptic shape of optic disc and extracts the Region of Interest (ROI) containing optic disc. The modified expectation maximization (MEM) algorithm is proposed to segment the hard exudates from the fundus image to identify the disease of diabetics. The Gray level Co-Occurrence Matrix (GLCM) and bandlet transform is applied to calculate the features for classification. The convolution neural network (CNN) is applied to classify the images into normal or abnormal.

     

     

     


  • Keywords


    Diabetic retinopathy, Image segmentation, Image enhancement, Principle Component Analysis, Curvelet Transform

  • References


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Article ID: 22981
 
DOI: 10.14419/ijet.v7i3.20.22981




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