title: Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas.
abstract: OBJECTIVE
  A computer-based image analysis system was developed for assessing the malignancy of urinary bladder carcinomas in a more objective manner. Tumours characterized in accordance with the WHO grading system were classified into low-risk (grades I and II) and high-risk (grades III and IV).
  
  
  METHODS
  Images from 92 haematoxylin-eosin stained sections of urinary bladder carcinomas were digitized and analysed. An adequate number of nuclei were segmented from each image for morphologic and textural analysis. Image segmentation was performed by an efficient algorithm, which used pattern recognition methods to automatically characterize image pixels as nucleus or background. Image classification into low-risk or high-risk tumours was performed by means of the quadratic non-linear Bayesian classifier, which was designed employing 36 textural and morphological features of the nucleus.
  
  
  RESULTS
  Automatic segmentation of nuclei on all images was about 90% on average. Overall system accuracy in correctly classifying tumours into low-risk or high-risk was 88%, employing the leave-one-out method and the best combination of three textural and one morphological feature. Classification accuracy for low-risk tumours was 88.8% and for high-risk tumours 86.2%.
  
  
  CONCLUSIONS
  The proposed image analysis system may be of value to the objective assessment of the malignancy of urine bladder carcinomas, since it relies on nuclear parameters that are employed in visual grading and their prognostic value has been proved.
authors.affiliation: Computer Laboratory, School of Medicine, University of Patras, Rio, Greece.
authors.forename: P
authors.initials: P
authors.lastname: Spyridonos
authors.forename: P
authors.initials: P
authors.lastname: Ravazoula
authors.forename: D
authors.initials: D
authors.lastname: Cavouras
authors.forename: K
authors.initials: K
authors.lastname: Berberidis
authors.forename: G
authors.initials: G
authors.lastname: Nikiforidis
mesh_terms: D000465:Algorithms; D004801:Eosine Yellowish-(YS); D006416:Hematoxylin; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015999:Multivariate Analysis; D013194:Staining and Labeling / Q000379:methods*; D014481:United States; D001749:Urinary Bladder Neoplasms / Q000145:classification / Q000473:pathology*
chemical_list: D004801:Eosine Yellowish-(YS); D006416:Hematoxylin