Improvement of Vessel Segmentation by Matched Filtering in Colour Retinal Images

A method for segmentation of vessel structure in colour retinal fundus images is presented, based on 2D matched filtering correlating the local image areas with 2D masks obtained via averaging of brightness profiles of vessels for several different vessel widths.Each of the basic masks is rotated in twelve different directions; this way, 60 masks for 5 different widths, each with 12 orientations are produced and used as 2D convolution kernels of the matched filters. The maximum response of all the filter responses for a concrete local area thus carries - if there is a vessel present - the information both on the width and orientation of the vessel segment. Compared to the previously published results [6], the segmentation has been improved primarily in two directions: the width resolution has been increased from 3 to 5 classes with a better approximation of the brightness profiles, and the orientation information is now utilized to provide vessel direction maps that are further used in the following phase of complementing the missing vessel segments. The parametric maps representing the maximum responses of the filters are then combined and finally tresholded thus obtaining binary vessel maps to be morphologically cleaned in order to remove the artefacts due to noise and also to complement the obviously missing parts of vessels. The method was designed and tested using the high-resolution fundus camera images provided by a cooperating ophthalmological clinic, and also statistically tested based on the standard public image database DRIVE.