@article{MAKHILLAJIT20201996804,
    title = {Detection of the Presence of Micro Aneurysms in Retinal Images using FCM},
    journal = {Asian Journal of Information Technology},
    volume = {19},
    number = {9},
    pages = {181-185},
    year = {2020},
    issn = {1682-3915},
    doi = {ajit.2020.181.185},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2020.181.185},
    author = {Kalla and},
    keywords = {Fuzzy c-means clustering,medical
decision-making,spatial information,anisotropic diffusion filter,fund us image,image classification,biomedical image processing,pattern recognition,non-local methodologies},
    abstract = {In diabetes retinopathy, identification of Micro
aneurysms is an important phase for treatment of diabetic
patients. The clustering technique which we are going to
present identifies micro aneurysms from optic disk in the
retinal fundus pictures. Constrained Spectral Clustering
(CSC) has shown great deal with images to identify MA
in medical retinal images with increasing accuracy in
image segmentation. CSC performs by integrating sparse
based coding to construct efficient frames based on
clustering in image segmentation to identify MAs in
retinal images for better image segmentation in pixel
calculation in large image data sets present in application
processing. So in this study, we extend CSC with Fuzzy
C-Means (FCM) Clustering. FCM is used to cluster the
data in which the dataset is grouped into n clusters. Every
data point of the dataset belongs to every cluster to a
certain degree. In FCM, we perform efficient clustering
with spatial pixel evaluation in retinal images with
diabetic retinal images to divide spectral coding in
medical decision analysis with preferable operations.}
    }