Abstract
In diabetic patients, blindness is caused commonly by a retinal disorder called Diabetic Retinopathy (DR). Cross-sectional area of the retina in the micrometer-level resolution can be imaged with the help of Optical Coherence tomography. The high-resolution retinal OCT image provides the information about the retina which is useful in the diagnosis and deciding the treatment of diabetic retinopathy. The aim of this project is to detect the diabetic retinopathy from optical coherence tomography image by identifying its features. Based on the gradient information, retinal layers in the optical coherence tomography image are segmented automatically. Seven retinal layers are segmented based on the Graph-Cut method. This Graph-Cut method algorithm is implemented on the optical coherence tomography image of normal subjects and diabetic retinopathy subjects. The features such as thickness of retinal layers and neovascularization are extracted from the segmented optical coherence tomography retinal layers. These distinct features show the difference between normal subjects and diabetic retinopathy subjects.
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