@article{MAKHILLJEAS202015819248,
    title = {Vascular Networks Segmented from Retinal Images of Hypertensive Retinopathy and
Glaucoma Patients},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {8},
    pages = {1932-1936},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.1932.1936},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.1932.1936},
    author = {Akande,Ayegba,Adegun,1Ogundokun,Asani,Abikoye,Gbadamosi and},
    keywords = {vascular networks,Glaucoma,hypertensive retinopathy,DRIONS-DB,segmentation,biomedical image},
    abstract = {Hypertensive Retinopathy (HR) and glaucoma
are two of the most common and leading eye problems
responsible for human vision loss and blindness. Both
cases cause alteration of vascular structures of the retina
thereby initiating a gradual vison loss and eventual
blindness. It is relieving to know that early detection of
the changes in the vascular structure of the retina can help
to detect these diseases before the eventual collapse of the
eye. This study presents a dataset that contains high
resolution biomedical image files of vascular structures
extracted from retinal images available in Digital Retinal
Images for Optic Nerve Segmentation Database
(DRIONS-DB). The database contains 110 retinal
images that were captured with HP-Photo Smart-S20
high-resolution scanner. The images are of 600×400
resolution and in JPEG format. Prior to extraction, the raw
images were preprocessed using median filter,
Mahalanobis distance and Contrast Limited Adaptive
Histogram Equalization (CLAHE). The blood vessel
segmentation was carried out using Dempster-Shafer
(D-S) edge based detector while MATLAB R2015a
programming environment was used for the
implementation.}
    }