Fulong Ren1,2, Wei Li2, Jinzhu Yang1,2, Huan Geng1,2, Dazhe Zhao1,2. 1. School of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, China. 2. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
Abstract
BACKGROUND: Existing methods may fail to locate and segment the optic disc (OD) due to imprecise boundaries, inconsistent image contrast and deceptive edge features in retinal images. OBJECTIVE: To locate the OD and detect the OD boundary accurately. METHODS: The method exploits a multi-stage strategy in the detection procedure. Firstly, OD location candidate regions are identified based on high-intensity feature and vessels convergence property. Secondly, a line operator filter for circular brightness feature detection is designed to locate the OD accurately on candidates. Thirdly, an initialized contour is obtained by iterative thresholding and ellipse fitting based on the detected OD position. Finally, a region-based active contour model in a variational level set formulation and ellipse fitting are employed to estimate the OD boundary. RESULTS: The proposed methodology achieves an accuracy of 98.67% for OD identification and a mean distance to the closest point of 2 pixels in detecting the OD boundary. CONCLUSION: The results illuminate that the proposed method is effective in the fast, automatic, and accurate localization and boundary detection of the OD. The present work contributes to the more effective evaluation of the OD and realizing automatic screening system for early eye diseases to a large extent.
BACKGROUND: Existing methods may fail to locate and segment the optic disc (OD) due to imprecise boundaries, inconsistent image contrast and deceptive edge features in retinal images. OBJECTIVE: To locate the OD and detect the OD boundary accurately. METHODS: The method exploits a multi-stage strategy in the detection procedure. Firstly, OD location candidate regions are identified based on high-intensity feature and vessels convergence property. Secondly, a line operator filter for circular brightness feature detection is designed to locate the OD accurately on candidates. Thirdly, an initialized contour is obtained by iterative thresholding and ellipse fitting based on the detected OD position. Finally, a region-based active contour model in a variational level set formulation and ellipse fitting are employed to estimate the OD boundary. RESULTS: The proposed methodology achieves an accuracy of 98.67% for OD identification and a mean distance to the closest point of 2 pixels in detecting the OD boundary. CONCLUSION: The results illuminate that the proposed method is effective in the fast, automatic, and accurate localization and boundary detection of the OD. The present work contributes to the more effective evaluation of the OD and realizing automatic screening system for early eye diseases to a large extent.