title: Quantitative assessment of liver fibrosis: a novel automated image analysis method.
abstract: BACKGROUND
  Semiquantitative staging of liver fibrosis is a highly subjective procedure and may lead to an uncertainty in judgment regarding the degree of severity and hence the progression of the disease.
  
  
  OBJECTIVE
  In this work, we present an automated quantification system (AQS) for evaluating the degree of severity of fibrosis in liver biopsies based on Ishak et al.'s classification. Accordingly, liver fibrosis is classified into six classes depending on its severity and progression. The described system is of special value in accurately assessing the prognosis of chronic liver disease.
  
  
  METHODS
  In our method, we tried to approximate the architecture of the fibrosis in the subject sample using texture features and shape representation of the fibrosis structural expansion with an overall accuracy of about 98%.
  
  
  CONCLUSIONS
  The presented AQS is considered to be a novel approach in the domain of automatic liver fibrosis quantification. It is a true quantification and intelligent approach that attempts to utilize the current semiquantitative methods of liver fibrosis assessment to turn them into real quantitative ones with significant reduction in variability and subjectivity. We propose that our method can be adopted by a panel of expert liver pathologists and software to be developed and used on a wide scale.
authors.affiliation: Department of Pathology, Jordan University of Science and Technology, Irbid, Jordan. imatalka@hotmail.com
authors.forename: Ismail I
authors.initials: II
authors.lastname: Matalka
authors.forename: Omar M
authors.initials: OM
authors.lastname: Al-Jarrah
authors.forename: Toqa M
authors.initials: TM
authors.lastname: Manasrah
mesh_terms: D001706:Biopsy; D018450:Disease Progression; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008103:Liver Cirrhosis / Q000473:pathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012720:Severity of Illness Index