Document Detail

Quantitative assessment of liver fibrosis: a novel automated image analysis method.
MedLine Citation:
PMID:  17032405     Owner:  NLM     Status:  MEDLINE    
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. AIM: 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%. RESULTS AND CONCLUSION: 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.
Ismail I Matalka; Omar M Al-Jarrah; Toqa M Manasrah
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Liver international : official journal of the International Association for the Study of the Liver     Volume:  26     ISSN:  1478-3223     ISO Abbreviation:  Liver Int.     Publication Date:  2006 Nov 
Date Detail:
Created Date:  2006-10-11     Completed Date:  2007-01-09     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101160857     Medline TA:  Liver Int     Country:  England    
Other Details:
Languages:  eng     Pagination:  1054-64     Citation Subset:  IM    
Department of Pathology, Jordan University of Science and Technology, Irbid, Jordan.
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MeSH Terms
Disease Progression
Image Processing, Computer-Assisted*
Liver Cirrhosis / pathology*
Neural Networks (Computer)
Pattern Recognition, Automated
Reproducibility of Results
Severity of Illness Index

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