Automated segmentation and quantitative analysis of optic disc and fovea in fundus images

Abstract

Fundus image is widely used diagnosis method and involves the retinal tissues which can be important biomarkers for diagnosing diseases. Many studies have proposed automatic algorithms to detect the optic disc (OD) and fovea. However, they showed some limitations. Although the precise regions of retinal tissues are clinically important, most of these studies focused on the localization not the segmentation. Also, they did not sufficiently prove the clinical effectiveness of the methods using quantitative analysis. Furthermore, many of them have researched about the single retinal tissue. To compensate for these limitations, this study proposed automated segmentation method for both of the OD and fovea. In this study, the dataset was acquired from the DRIVE and Drions databases, and additional ground truth dataset was obtained from an ophthalmologist. The original fundus image was preprocessed to remove noise and enhance contrast. And the retinal vessel was segmented to use for the OD and fovea segmentation. In the OD and fovea segmentation step, a region of interest was designated based on the image features to increase the segmentation accuracy. To segment the OD, the retinal vessel was removed and substituted based on the intensity value of the four nearest non-vessel pixels. Finally, the OD and fovea regions were segmented based on the image features including intensity, shape and size. The proposed method was evaluated by quantitative analysis using eight methods. As a result, the proposed method showed high segmentation performance for the OD and fovea with accuracy of 99.18 and 99.80 % on the DRIVE database.