Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.

Accurate optic disc localization and segmentation are two main steps when designing automated screening systems for diabetic retinopathy. In this paper, a novel optic disc detection approach based on saliency object detection and modified local intensity clustering model is proposed. It consists of two stages: in the first stage, the saliency detection technique is introduced to the enhanced retinal image with the aim of locating the optic disc. In the second stage, the optic disc boundary is extracted by the modified Local Intensity Clustering (LIC) model with oval-shaped constrain. The performance of our proposed approach is tested on the public DIARETDB1 database. Compared to the state-of-the-art approaches, the experimental results show the advantages and effectiveness of the proposed approach.