Automatic detection of retinal vascular landmark features for colour fundus image matching and patient longitudinal study

Retinal vascular landmark points such as branching points and crossovers are important features for automatic retinal image matching and vascular abnormality detection. These landmark points can enable automatic screening of large dataset through the detection of vascular network abnormalities (i.e., arteriovenous nicking, retinal vein occlusion) which are important for hypertension and cardiovascular disease prediction. Existing methods for crossover point detection use only local information at each image pixel without considering vascular features to detect crossover positions. This leads to the misclassification of very acute crossovers which are represented by two bifurcation points in the skeleton image. In this article, we propose a robust method that utilizes both local information and vascular geometrical features at the crossing to distinguish crossover from non-crossover points in a retinal image. The proposed method was validated on fifteen high resolution retinal images and the results show that our method achieves higher accuracy than any existing methods. In particular, the proposed method can discover more than 74% (recall) of crossovers with a detection accuracy (fraction of detected crossover points that are correct) of 83% (precision). The detected crossovers provide essential results for the automatic detection of vascular network abnormalities, such as arteriovenous nicking, neovascularization, and retinal vein occlusion.