Unsupervised retinal vessel segmentation using combined filters

Abstract

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combinedmatched filter, Frangi's filter and GaborWavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work.We investigate two approaches to performthe filter combination: weighted mean andmedian ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The firstmethod is 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 experimental results demonstrate that the proposedmethods performwell for vessel segmentation in comparison with state-of-the-art methods.