Improved Vessel Segmentation Using Curvelet Transform and Line Operators

Vessel segmentation from the fundus retinal images is highly significant in diagnosing many pathologies related to eye and other systemic diseases. Even though there are many methods in the literature focusing on this task, most of these methods are not focusing on the small peripheral vessels segmentation. In this paper, we propose a new approach based on curvelet transform and line operators which can segment the small peripheral vessels with very high accuracy resulting in a higher sensitivity compared to the other state-of-the-art methods. In the proposed approach, the contrast between the retinal vessels and the background pixels is enhanced by applying a series of image processing steps involving color space transformation, adaptive histogram equalization, and anisotropic diffusion filtering. Then by using the modified curvelet transform coefficients, the retinal vessel edge contrast is further enhanced. Finally, the vessels are segmented out by applying the line operator response, followed by suitable thresholding to obtain the segmented vessels. Post processing is carried out to remove the scattered unwanted background pixels. The performance of the method is compared against the other state-of-the-art methods using DRIVE as a testing database. An average sensitivity, specificity, accuracy and positive predictive value of 0.7653, 0.9735, 0.9542 and 0.7438 are respectively achieved.