title: Computer-aided classification of centroblast cells in follicular lymphoma.
abstract: OBJECTIVE
  To distinguish centroblast cells from non-centroblast cells using a novel automated method in follicular lymphoma cases and measure its performance on cases obtained by a consensus of six pathologists.
  
  
  METHODS
  Geometric and color texture features were used in the training and testing of the supervised quadratic discriminant analysis classifier. The technique was trained and tested on a data set composed of 218 centroblast images and 218 non-centroblast images. Computer performance was tested by measuring sensitivity and specificity among cells classified as centroblast and non-centroblast by consensus of six board-certified hematopathologists.
  
  
  RESULTS
  Automated classification distinguished centroblast from non-centroblast cells with a classification accuracy of 82.56% and sensitivity and specificity of 86.67% and 86.96%, respectively, when the approach was tested.
  
  
  CONCLUSIONS
  The novelty of our approach is the identification of the centroblast cells with prior information and the introduction of the principal component analysis in the spectral domain to extract texture color features.
authors.affiliation: Department of Biomedical Informatics, College of Public Health, The Ohio State University, 333 West 10th Avenue, Columbus, Ohio 43210, USA. kamel.boussaid@osumc.edu
authors.forename: Kamel
authors.initials: K
authors.lastname: Belkacem-Boussaid
authors.forename: Michael
authors.initials: M
authors.lastname: Pennell
authors.forename: Gerard
authors.initials: G
authors.lastname: Lozanski
authors.forename: Arwa
authors.initials: A
authors.lastname: Shana'ah
authors.forename: Metin
authors.initials: M
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D003116:Color; D016002:Discriminant Analysis; D006801:Humans; D008224:Lymphoma, Follicular; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity