Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy

Proliferative diabetic retinopathy can lead to blindness. However early recognition allows appropriate, timely intervention. Fluorescein-labelled retinal blood vessels of twenty-seven digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter and an additional five morphological features based on the derivatives-of-Gaussian wavelet derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73'0.97 95% CI). The wavelet method was able to segment retinal blood vessels and classify the images into proliferative retinopathy present or absent.