An efficient blood vessel detection algorithm for retinal images using local entropy thresholding

This paper presents an efficient method for automatic detection and extraction of blood vessels in retinal images. Specifically, we also delineate vascular intersections/crossovers. The proposed algorithm is composed of four steps: matched filtering, local entropy thresholding, length filtering, and vascular intersection detection. The purpose of matched filtering is to enhance the blood vessels. Entropy-based thresholding can well keep the spatial structure of vascular tree segments. Length filtering is used to remove misclassified pixels. The algorithm has been tested on twenty ocular fundus images, and experimental results are compared with those obtained from a state-of-the-art method and hand-labeled ground truth segmentations.