@article{MAKHILLAJIT20131245753,
    title = {Segmentation of CSF in MRI Brain Images Using Optimized Clustering Methods},
    journal = {Asian Journal of Information Technology},
    volume = {12},
    number = {4},
    pages = {109-116},
    year = {2013},
    issn = {1682-3915},
    doi = {ajit.2013.109.116},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2013.109.116},
    author = {P. Tamije,V. and},
    keywords = {Cerebrospinal fluid,segmentation,magnetic resonance image,fuzzy C means,total variation regularizer,anisotropic diffusion,particle swarm optimization},
    abstract = {Image segmentation is an indispensible part of the visualization 
  of human tissues during the analysis of Magnetic Resonance Imaging (MRI). MRI 
  is an advanced medical imaging technique which provides rich information for 
  detecting Cerebrospinal Fluid (CSF) in brain images. The changes in the CSF 
  protein level forms abnormal brain deposits strongly linked to variety of neurological 
  diseases. The traditional clustering methods yield more false positives. The 
  proposed system encompasses the following steps, Pre-Processing the MRI brain 
  images using Contrast Limited Adaptive Histogram Equalization, Clustering by 
  Fuzzy C Means (FCM), Total Variation FCM (TVFCM), Anisotropic Diffused TVFCM 
  (ADTVFCM), Optimizing the clustering techniques using Particle Swarm Optimization 
  (PSO) (FCM-PSO, TVFCM-PSO and ADTVFCM-PSO). The clustering methods provide only 
  local optimal solution. In order to achieve global optimal solution, the clustering 
  methods are further optimized using PSO. The performance of the optimized clustering 
  methods is measured using defined set of Simulated MS Lesion Brain database. 
  The optimized clustering methods finds the CSF present in MRI brain images with 
  98% of accuracy, 92% of sensitivity and 97% of specificity.}
    }