Unsupervised delineation of the vessel tree in retinal fundus images

Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.