Blood Vessel Segmentation from Retinal Images

Retinal image analysis is increasingly important for diagnosing eye diseases, and blood vessels are one of the most important indicators. This paper presents an automated and unsupervised method for segmenting retinal blood vessels from fundus images by using the level set method, which adopts Chan-Vese region-based term with a Gaussian mixture term and a distance regularisation term. Also included in the method are the morphological closing operation and matched filtering to preserve the vessels inside the optic disc and remove the noise of the optic disc boundary, and to enhance the blood vessel information. The effectiveness of this method is demonstrated through testing and comparing with the state-of-the-art methods on three public datasets DRIVE, STARE and HRF. The experimental results show that our method offers several advantages over other methods, in particular in dealing with interference from the optic disc, segmenting vessels inside the optic disc and segmenting small vessel branches.