Extraction of Retinal Blood Vessel Using Curvelet Transform and Fuzzy C-Means

This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curve let transform and fuzzy c-means. Although noise is kept constant in medical CCD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of the images looks very poor. Some form of pre-processing operation is therefore essential for the accurate extraction of these blood vessels. Since curve let transform can represent lines, edges and curvatures very well as compared to other multi-resolution techniques, this paper uses curve let transform to enhance the retinal vasculature. Matched filtering is then used to intensify the blood vessels which is further employed by fuzzy c-means algorithm to extract the vessel silhouette from the background. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methodology that uses curve let transform. Simulation results demonstrate that the proposed method is very much efficient in detecting long and thick as well as short and thin vessels, wherein the existing methods fail to extract tiny and thin vessels.