title: Brain Tissue Classification Based on Diffusion Tensor Imaging: A Comparative Study Between Some Clustering Algorithms and Their Effect on Different Diffusion Tensor Imaging Scalar Indices.
abstract: BACKGROUND
  Brain segmentation from diffusion tensor imaging (DTI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) with acceptable results is subjected to many factors.
  
  
  OBJECTIVE
  The most important issue in brain segmentation from DTI images is the selection of suitable scalar indices that best describe the required tissue in the images. Specifying suitable clustering method and suitable number of clusters of the selected method are other factors which affects the segmentation process significantly.
  
  
  METHODS
  The segmentation process is evaluated using four different clustering methods with different number of clusters where some DTI scalar indices for 10 human brains are processed.
  
  
  RESULTS
  The aim was to produce results with less segmentation error and a lower computational cost while attempting to minimizing boundary overlapping and minimizing the effect of artifacts due to macroscale scanning.
  
  
  CONCLUSIONS
  The volume ratios of the best produced outputs with respect to the total brain size are 16.7% ± 3.53% for CSF, 35.05% ± 1.13% for WM, and 48.2% ± 2.88% for GM.
authors.affiliation: Computer Engineering Department, Faculty of Engineering, Adnan Menderes University, Aydin, Turkey.
authors.forename: Ihab
authors.initials: I
authors.lastname: Elaff
keywords: Brain Segmentation; Clustering; DTI; Diffusion Tensor; Diffusion Volume