FMRI-based hierarchical SVM model for the classification and grading of liver fibrosis

Yehonatan Sela*, Moti Freiman, Elia Dery, Yifat Edrei, Rifaat Safadi, Orit Pappo, Leo Joskowicz, Rinat Abramovitch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


We present a novel method for the automatic classification and grading of liver fibrosis based on hepatic hemodynamic changes measured noninvasively from functional MRI (fMRI) scans combined with hypercapnia and hyperoxia. The supervised learning method automatically creates a classification and grading model for liver fibrosis grade from training datasets. It constructs a statistical model of liver fibrosis by evaluating the signal intensity time course and local variance in T2 *-W fMRI scans acquired during the breathing of air, air-carbon dioxide, and carbogen with a hierarchical multiclass binary-based support vector machine (SVM) classifier. Two experimental studies on 162 slices from 34 mice with the hierarchical multiclass binary-based SVM classifier yield 96.9 separation accuracy between healthy and histological-based fibrosis graded subjects, and an overall accuracy of 75.3 for healthy, fibrotic, and cirrhotic subjects. These results outperform existing image-based methods that can discriminate between healthy and mild-grade fibrosis subjects.

Original languageAmerican English
Article number5875869
Pages (from-to)2574-2581
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number9
StatePublished - Sep 2011
Externally publishedYes


  • Abdominal
  • characterization
  • early detection
  • fibrosis
  • functional MRI (fMRI)
  • liver
  • machine learning


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