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What is A Supervised Blood Vessel Segmentation Technique For Digital Fundus Images Using Zernike Moment Based Features - Dharmateja Adapa 2020 about?

This research article presents a supervised blood vessel segmentation technique for digital fundus images utilizing Zernike moment-based features. The method employs a pixel-wise classification approach, achieving high accuracies of 0.945 and 0.9486 on the DRIVE and STARE databases, respectively, while effectively identifying thinner blood vessels. The study demonstrates improved performance over existing methods, aiding in the early detection of various retinal pathologies.

Author

soorajsruthi

Language

EN