Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data

Jingjing Gao, Chunming Li, Chaolu Feng, Mei Xie, Yilong Yin, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Segmentation of multiple sclerosis (MS) lesion is important for many neuroimaging studies. In this paper, we propose a novel algorithm for automatic segmentation of MS lesions from multi-channel MR images (T1W, T2W and FLAIR images). The proposed method is an extension of Li et al.'s algorithm in [1], which only segments the normal tissues from T1W images. The proposed method is aimed to segment MS lesions, while normal tissues are also segmented and bias field is estimated to handle intensity inhomogeneities in the images. Another contribution of this paper is the introduction of a nonlocal means technique to achieve spatially regularized segmentation, which overcomes the influence of noise. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.

Original languageEnglish (US)
Pages (from-to)1058-1066
Number of pages9
JournalMagnetic Resonance Imaging
Issue number8
StatePublished - Oct 2014


  • Bias field estimation
  • Energy minimization
  • Lesion segmentation
  • Multi-channel MR images
  • Nonlocal means

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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