Automated Blood Vessel Segmentation of Fundus Images Using Region Features of Vessels

This paper proposes a novel and simple unsupervised vessel segmentation algorithm using fundus images. At first, the green channel of a fundus image is preprocessed to extract a binary image after the isotropic undecimated wavelet transform, and another binary image from the morphologically reconstructed image. Secondly, two initial vessel images are extracted according to the vessel region features for the connected regions in binary images. Next, the regions common to both initial vessel images are extracted as the major vessels. Then all remaining pixels in two initial vessel images are processed with skeleton extraction and simple linear iterative clustering. Finally the major vessels are combined with the processed vessel pixels. The proposed algorithm outperforms its competitors when compared with other widely used unsupervised and supervised methods, which achieves a vessel segmentation accuracy of 95.8% and 95.8% in an average time of 9.7s and 14.6s on images from two public datasets DRIVE and STARE, respectively.