title: Automated image analysis of nuclear atypia in high-power field histopathological image.
abstract: OBJECTIVE
  We developed a computer-aided technique to study nuclear atypia classification in high-power field haematoxylin and eosin stained images.
  
  
  RESULTS
  An automated technique for nuclear atypia score (NAS) calculation is proposed. The proposed technique uses sophisticated digital image analysis and machine-learning methods to measure the NAS for haematoxylin and eosin stained images. The proposed technique first segments all nuclei regions. A set of morphology and texture features is extracted from presegmented nuclei regions. The histogram of each feature is then calculated to characterize the statistical information of the nuclei. Finally, a support vector machine classifier is applied to classify a high-power field image into different nuclear atypia classes. A set of 1188 digital images was analysed in the experiment. We successfully differentiated the high-power field image with NAS1 versus non-NAS1, NAS2 versus non-NAS2 and NAS3 versus non-NAS3, with area under receiver-operating characteristic curve of 0.90, 0.86 and 0.87, respectively. In three classes evaluation, the average classification accuracy was 78.79%. We found that texture-based feature provides best performance for the classification.
  
  
  CONCLUSIONS
  The automated technique is able to quantify statistical features that may be difficult to be measured by human and demonstrates the future potentials of automated image analysis technique in histopathology analysis.
authors: College of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi Province, China.
authors: Cheng
authors: C
authors: Lu
authors: Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
authors: Mengyao
authors: M
authors: Ji
authors: College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi Province, China.
authors: Zhen
authors: Z
authors: Ma
authors: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada.
authors: Mrinal
authors: M
authors: Mandal
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000648:ultrastructure*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012372:ROC Curve; D060388:Support Vector Machine
keywords: Classification; computer-aided image analysis; histopathological image analysis; nuclei atypia