title: Improved cerebellar tissue classification on magnetic resonance images of brain.
abstract: OBJECTIVE
  To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.
  
  
  METHODS
  Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices.
  
  
  RESULTS
  The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology.
  
  
  CONCLUSIONS
  The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.
authors.affiliation: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Houston, Texas 77030, USA. Sushmita.Datta@uth.tmc.edu
authors.forename: Sushmita
authors.initials: S
authors.lastname: Datta
authors.forename: Guozhi
authors.initials: G
authors.lastname: Tao
authors.forename: Renjie
authors.initials: R
authors.lastname: He
authors.forename: Jerry S
authors.initials: JS
authors.lastname: Wolinsky
authors.forename: Ponnada A
authors.initials: PA
authors.lastname: Narayana
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002531:Cerebellum / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity