An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters

  • Authors:

  • U. Rajendra Acharya;E. Y. Ng;Jen-Hong Tan;S. Vinitha Sree;Kwan-Hoong Ng

  • Affiliations:

  • Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore;School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore 639798;School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore 639798;School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore 639798;Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2012

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Abstract

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.