Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification

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BLOOD VESSEL SEGMENTATION OF FUNDUS IMAGES BY MAJOR VESSEL
EXTRACTION AND SUBIMAGE CLASSIFICATION
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 preprocessed 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
eight features that are extracted based on pixel neighborhood and first and second-order gradient
images. In the third postprocessing 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 asimages 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, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and
CHASE_DB1, respectively.

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