Morphological structure reconstruction of retinal vessels in fundus images

Vessels in retinal fundus images are useful in revealing the severity of eye-related diseases. In addition, they can act as landmarks for localizing lesions or the central vision area, and guide laser treatment of neovascularization. In this paper, we propose a two-stage scheme to extract vessels and reconstruct the morphological structure of vessels in retinal images. First, we employ mathematical morphology techniques to highlight large and small vessels with respect to their spatial properties. Different curvature response between vessel and noise patterns allows the use of curvature evaluation to remove enhanced vessel-like noise. A set of linear filters finalize the vessel map. However, the resulting vascular structure is incomplete of some important features in bifurcation points and central reflex. In order to rectify the pitfall, a reconstruction process is performed using dynamic local region growth to recover the morphological structure of vessels. Average performance of our method to extract vessels is 83.7% of TPR(True positive rate) and 3.8% of FPR(False positive rate) for 35 retinal images which include 21 abnormal images.