Automated grading of venous beading.

The degree of venous beading in ocular fundus images has been shown to be a more powerful predictor of conversion to proliferative diabetic retinopathy than any other type of retinal abnormality. Further, the degree of venous beading has been shown to be well correlated with disease progression. An algorithm for automated grading of venous beading in digitized ocular fundus images is described. Thresholding is used to extract a rough silhouette of the vein. Morphological closing is used to fill any holes in the silhouette arising from either the central light reflex or noise. The silhouette is then "thinned" to find vein centerlines. Each centerline is partitioned into fixed-length segments of 32 pixels. Vein diameters are measured as a function of distance along each segment with the aid of the local centerline orientations. The resulting diameter data are then interpolated and resampled to generate diameter data at constant sampling intervals. A fast Fourier transform is performed on the resulting data to determine the magnitude spectrum of vein segment diameter. A venous beading index is calculated from the distribution of vein diameter frequency components. Performance of the new algorithm is compared to the currently accepted clinical practice of manual grading in a pilot clinical study of 51 subjects. The algorithm is seen to perform well.