@article{MAKHILLAJIT20151445929,
    title = {Segmentation of Cervical Image Using Unsupervised Clustering Algorithms with
L*u*v Color Transformation},
    journal = {Asian Journal of Information Technology},
    volume = {14},
    number = {4},
    pages = {147-153},
    year = {2015},
    issn = {1682-3915},
    doi = {ajit.2015.147.153},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2015.147.153},
    author = {Anantha Sivaprakasam and},
    keywords = {Fast K-means,fuzzy c-mean,L*u*v color transformation,image segmentation,clustering},
    abstract = {This study deals with the fast and efficient algorithm for segmenting the cervical image using
unsupervised methods. Here, the image segmentation method is based on basic region growing method. In this
study, both fast k-means with optional weighting and careful initialization and Fuzzy c-means Clustering
algorithm are used to deal with segmentation of the cervical image. Both algorithm segments the image in
accordance with the colour for each cluster and its neighborhood. In this method, first the cervical image is
smoothed, enhanced and converted into L*u*v color space. The L*u*v color space image is segmented using
fast k-means algorithms with optional weighting and careful seeding and fuzzy c-means algorithm. Finally, the
performance analysis of the three segmentation algorithms is carried out. Experimental results show that
fast k-means segmentation with careful seeding methods are fast as compare to Fast K-means with weight and
fuzzy c-means method, three algorithm gives better segmented images with finer details and accurate location
but FCM takes more time.}
    }