Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classi?cation

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Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classi?cation

Abstract?A novel three-stage blood vessel segmentation algorithm using fundus photographs. In the ?rst stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass ?ltering, 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 classi?ed using a Gaussian mixture model < Final Year Projects 2016 > GMM classi?er using a set of eight features that are extracted based on pixel neighborhood and ?rst and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classi?ed 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, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.

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Digital Image Processing

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