title: Automated feature extraction and identification of colon carcinoma.
abstract: OBJECTIVE
  To assess an automated algorithm, developed for the classification of normal and cancerous colonic mucosa, using geometric analysis of features and texture analysis.
  
  
  METHODS
  Twenty-one images were analyzed, 10 from normal and 11 from cancerous mucosa. The classification was based on a regularity index dependent on shape, object orientation for establishing parallelism and five texture features derived using the co-occurrence image analysis method.
  
  
  RESULTS
  Geometric analysis yielded an overall classification accuracy of 80%. The corresponding sensitivity and specificity were 94% and 64%, respectively. Using texture analysis, the overall classification accuracy was 90%, with a sensitivity and specificity of 82% and 100%, respectively.
  
  
  CONCLUSIONS
  This initial study demonstrated that geometric and texture analysis techniques show promise for automated analysis of colon cancer.
authors.affiliation: Department of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.
authors.forename: A N
authors.initials: AN
authors.lastname: Esgiar
authors.forename: R N
authors.initials: RN
authors.lastname: Naguib
authors.forename: M K
authors.initials: MK
authors.lastname: Bennett
authors.forename: A
authors.initials: A
authors.lastname: Murray
mesh_terms: D000465:Algorithms; D002277:Carcinoma / Q000175:diagnosis* / Q000473:pathology; D003110:Colonic Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D019044:Image Cytometry / Q000379:methods* / Q000706:statistics & numerical data; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015203:Reproducibility of Results