Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

M. Freiman*, Y. Sela, Y. Edrei, O. Pappo, L. Joskowicz, R. Abramovitch

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2∗-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Original languageAmerican English
Title of host publicationMedical Imaging 2010
Subtitle of host publicationComputer-Aided Diagnosis
EditorsRonald M. Summers, Nico Karssemeijer
ISBN (Electronic)9780819480255
StatePublished - 2010
EventMedical Imaging 2010: Computer-Aided Diagnosis - San Diego, United States
Duration: 16 Feb 201018 Feb 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2010: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Publisher Copyright:
© 2010 SPIE.


  • Abdominal
  • Characterization
  • Machine Learning


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