Extraction of retinal blood vessels by curvelet transform

Abstract

This paper presents an efficient method for automatic extraction of blood vessels in retinal images to improve the detection of low contrast and narrow vessels. The proposed algorithm is composed of four steps: curvelet-based contrast enhancement, match filtering, curvelet-based edge extraction, and length filtering. In this base, after reconstruction of enhanced image from the modified curvelet coefficients, match filtering is used to intensify the blood vessels. Then we employ curvelet transform to segment vessels from its background and finally the length filtering is used to remove the misclassified pixels. The performance of algorithm is evaluated on DRIVE [1] databases and compared with those obtained from a hand-labeled ground truth. Since the curvelet transform is well-suited to handle curve discontinuities, we achieve an area under ROC curve of 0.9631 that demonstrates improved performance of proposed algorithm compared with known techniques.