A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Abstract

Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.