title: Textural analysis in the diagnosis of benign and malignant breast cells.
abstract: OBJECTIVE
  To study the discriminatory capacity of textural variables to classify the nuclei of breast tumor cells as benign or malignant, using a statistical approach.
  
  
  METHODS
  Image analysis techniques were used to automatically segment nuclei of cells obtained by fine needle aspiration and Papanicolaou stained. The sample comprised 95 cases of malignant lesions and 47 cases of benign lesions (approximately 25 nuclei per case), and 27 textural variables were measured. Two methods were used to analyze the data: classification and regression trees (CART) and discriminant analysis.
  
  
  RESULTS
  The variance in gray levels was the most decisive variable in the CART analysis, correctly classifying 57% and 97% of benign and malignant cases, respectively. Discriminant analysis yielded the best results, correctly classifying 79% and 85% of benign and malignant cases, respectively.
  
  
  CONCLUSIONS
  The classifier obtained by a statistical approach to the textural analysis of Papanicolaou-stained nuclei did not prove useful for diagnostic discrimination. Staining techniques that are not chromatin specific are highly variable, and other features have proven more effective with this type of staining.
authors.affiliation: Research, Quality and Teaching Unit, Virgen del las Nieves University Hospital, Granada, Spain. cherrerae@fundacionhvn.org
authors.forename: Carmen
authors.initials: C
authors.lastname: Herrera Espiñeira
authors.forename: Joaquín
authors.initials: J
authors.lastname: Fernández Valdivia
authors.forename: Javier Esquivias
authors.initials: JE
authors.lastname: López-Cuervo
authors.forename: Jesús
authors.initials: J
authors.lastname: Martínez Tapias
mesh_terms: D000465:Algorithms; D001707:Biopsy, Needle; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis / Q000473:pathology; D002467:Cell Nucleus / Q000473:pathology; D016002:Discriminant Analysis; D005260:Female; D018226:Fibroadenoma / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D019044:Image Cytometry; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis* / Q000473:pathology; D065006:Papanicolaou Test; D012044:Regression Analysis; D012189:Retrospective Studies; D014626:Vaginal Smears