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Analysis of Foveal Avascular Zone for Grading of Diabetic Retinopathy Severity Based on Curvelet Transform Publisher Pubmed



Alipour SHM1 ; Rabbani H1 ; Akhlaghi M2 ; Dehnavi AM3 ; Javanmard SH4
Authors
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Authors Affiliations
  1. 1. Biomedical Engineering Department, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Ophthalmology Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Biomedical Engineering Department, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Physiology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Graefe's Archive for Clinical and Experimental Ophthalmology Published:2012


Abstract

Introduction: Diabetes disturbs many parts of the body. One of the most common and serious complications of this disease is Diabetic Retinopathy (DR). In this process, blood vessels of the retina are damaged and leak into the retina. In later stages, DR affects the fovea. In these cases, the shape and size of the Foveal Avascular Zone (FAZ), which is responsible for central vision, can become abnormal and contribute to loss of vision. Methods: In this paper, appropriate features are extracted from the FAZ by means of Digital Curvelet Transform (DCUT) and used to grade of retina images into normal and abnormal classes. For this reason, DCUT is applied on enhanced color fundus images and its coefficients are modified to highlight vessels and the optic disc (OD). Through the use of this information about the anatomical location of the FAZ related to the OD and detected end points of segmented vessels, the FAZ is extracted. Then, the area and regularity of the extracted FAZ is determined and used for DR grading. Results: Our method was tested on a database including 45 normal and 30 abnormal color fundus images, and showed sensitivity of 93% for DR grading and specificity of 86% for distinguishing between normal and abnormal cases. Conclusions: This technique showed high reproducibility in characterizing the size and contour of the FAZ in diabetic maculopathy, thus it has the potential to serve as a powerful tool in the automated assessment and grading of images in a routine clinical setting. © Springer-Verlag 2012.
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