Optic Disc Localization using Fuzzy C Mean and DB Scan Clustering - A Comparative Analysis
In the diagnosis and early detection of Glaucoma and diabetic retinopathy, when delicate vasculature grows in the retina, precise identification and localization of the border optic disc are highly significant. This research provides an automated method to localize and detect the optic disc. The proposed method uses the clustering methodology to locate the optic disc region. Fuzzy C Mean and Density Based Scan (DB Scan) clustering approach is evaluated on the publicly accessible DRIVE, diaretdb1, diaretdb0, and databases, which were created to aid comparative investigations on optic disc localization and detection in retinal images. With Diaretdb0 and Drive DB, the DB Scan clustering approach obtained an accuracy of 94.11% and 81.18%, respectively, which is better than the Fuzzy C mean, and it performs DB scan better for the DiaretDB1 dataset.
@article{prakash2023,
author = {J. Prakash and B. Vinoth Kumar},
title = {{Optic Disc Localization using Fuzzy C Mean and DB Scan Clustering - A Comparative Analysis}},
journal = {Journal of Soft Computing Paradigm},
volume = {5},
number = {1},
pages = {22-36},
year = {2023},
publisher = {IRO Journals},
doi = {10.36548/jscp.2023.1.003},
url = {https://doi.org/10.36548/jscp.2023.1.003}
}
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