title: Classification of histological images of the endometrium using texture features.
abstract: OBJECTIVE
  To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium.
  
  
  METHODS
  A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features.
  
  
  RESULTS
  The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%.
  
  
  CONCLUSIONS
  The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.
authors.affiliation: School of Electrical and Computer Engineering, National Technical University of Athens, Greece. aleka16381@hotmail.com
authors.forename: Alexandra A
authors.initials: AA
authors.lastname: Vlachokosta
authors.forename: Pantelis A
authors.initials: PA
authors.lastname: Asvestas
authors.forename: George K
authors.initials: GK
authors.lastname: Matsopoulos
authors.forename: Agatha
authors.initials: A
authors.lastname: Kondi-Pafiti
authors.forename: Nikos
authors.initials: N
authors.lastname: Vlachos
mesh_terms: D000328:Adult; D016889:Endometrial Neoplasms / Q000145:classification* / Q000473:pathology*; D004717:Endometrium / Q000473:pathology*; D005260:Female; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity