Abstract

This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is pre-processed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions.

Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian Mixture Model (GMM) classifier using a set of 8 features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third post-processing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods.

The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15% and 95.3% in an average of 3.1 seconds, 6.7 seconds and 11.7 seconds on three public data sets DRIVE, STARE, and CHASE DB1, respectively.