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.

Image segmentation is an essential technique of image analysis. In spite of the issues in contour initialization and boundary concavities, active contour models (snakes) are popular and successful methods for segmentation. In this paper, we present a new active contour model, Gaussian Gradient Force snake (GGF snake), for segmentation of an endoscopic image. The GGF snake is less sensitive to contour initialization and it ensures a high accuracy, large capture range, and fast CPU time for computing an external force. It was observed that the GGF snake produced more reasonable results in various image types : simple synthetic images, commercial digital camera images, and endoscopic images, than previous snakes did.