Brain tumor segmentation from multimodal magnetic resonance images via sparse representation

Yuhong Li, Fucang Jia, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

56 Citations (Scopus)

Abstract

Objective Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. Methods We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Results Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. Conclusions The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalArtificial Intelligence in Medicine
Volume73
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • Brain tumor segmentation
  • Dictionary learning
  • Graph cuts
  • Markov random field
  • Multimodal magnetic resonance images
  • Sparse representation

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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