An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters

Image segmentation of retinal blood vessels is an important procedure for the prediction and diagnosis of cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work develops an unsupervised segmentation procedure for the segmentation of retinal vessels images using a combined matched filter, Frangi filter and Gabor Wavelet Filter. After the vessel enhancement, two segmentation methods are tested. The first method uses an approach based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.