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.
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.
Authors: O Arend; S Wolf; A Remky; W E Sponsel; A Harris; B Bertram; M Reim Journal: Graefes Arch Clin Exp Ophthalmol Date: 1994-04 Impact factor: 3.117
Authors: C P Wilkinson; Frederick L Ferris; Ronald E Klein; Paul P Lee; Carl David Agardh; Matthew Davis; Diana Dills; Anselm Kampik; R Pararajasegaram; Juan T Verdaguer Journal: Ophthalmology Date: 2003-09 Impact factor: 12.079
Authors: Yansha Lu; Joseph M Simonett; Jie Wang; Miao Zhang; Thomas Hwang; Ahmed M Hagag; David Huang; Dengwang Li; Yali Jia Journal: Invest Ophthalmol Vis Sci Date: 2018-05-01 Impact factor: 4.799