Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts

Dwarikanath Mahapatra, Peter J. Schuffler, Jeroen A.W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann

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

8 Scopus citations

Abstract

We propose a graph cut based method to segment regions in abdominal magnetic resonance (MR) images affected with Crohn's disease (CD). Intensity, texture, curvature and context information are used with random forest (RF) classifiers to calculate probability maps for graph cut segmentation. The RF classifiers also provide semantic information used to design a novel smoothness cost. Experimental results on 26 real patient data shows our method accurately segments the diseased areas. Inclusion of semantic information significantly improves segmentation accuracy and its importance is reflected in quantitative measures and visual results.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages358-361
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 7 Apr 201311 Apr 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period7/04/1311/04/13

Keywords

  • Crohn Disease
  • MRI
  • Random forests
  • context
  • graph cut
  • semantic information

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