Improved detection of the central reflex in retinal vessels using a generalized dual-gaussian model and robust hypothesis testing.

This updates an earlier publication by the authors describing a robust framework for detecting vasculature in noisy retinal fundus images. We improved the handling of the "central reflex" phenomenon in which a vessel has a "hollow" appearance. This is particularly pronounced in dual-wavelength images acquired at 570 and 600 nm for retinal oximetry. It is prominent in the 600 nm images that are sensitive to the blood oxygen content. Improved segmentation of these vessels is needed to improve oximetry. We show that the use of a generalized dual-Gaussian model for the vessel intensity profile instead of the Gaussian yields a significant improvement. Our method can account for variations in the strength of the central reflex, the relative contrast, width, orientation, scale, and imaging noise. It also enables the classification of regular and central reflex vessels. The proposed method yielded a sensitivity of 72% compared to 38% by the algorithm of Can et al., and 60% by the robust detection based on a single-Gaussian model. The specificity for the methods were 95%, 97%, and 98%, respectively.