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Tissue Segmentation Using Sparse Non-negative Matrix Factorization of Spherical Mean Diffusion MRI Data

  • Peng Sun
  • Ye Wu
  • Geng Chen
  • Jun Wu
  • Dinggang Shen
  • Pew-Thian YapEmail author
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

In this paper, we present a method based on sparse non-negative matrix factorization (NMF) for brain tissue segmentation using diffusion MRI (DMRI) data. Unlike existing NMF-based approaches, in our method NMF is applied to the spherical mean data, computed on a per-shell basis, instead of the original diffusion-weighted images. This is motivated by the fact that the spherical mean is independent of the fiber orientation distribution and is only dependent on tissue microstructure. Applying NMF to the spherical mean data will hence allow tissue signal separation based solely on the microstructural properties, unconfounded by factors such as fiber dispersion and crossing. We show results explaining why applying NMF directly on the diffusion-weighted images fails and why our method is able to yield the expected outcome, producing tissue segmentation with greater accuracy.

Keywords

Tissue segmentation Sparse NMF Spherical mean Diffusion MRI 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peng Sun
    • 1
    • 2
  • Ye Wu
    • 1
    • 3
  • Geng Chen
    • 1
  • Jun Wu
    • 4
  • Dinggang Shen
    • 1
  • Pew-Thian Yap
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  2. 2.School of Computer ScienceNorthwestern Polytechnical UniversityXi AnChina
  3. 3.Institute of Information Processing and Automation, Zhejiang University of TechnologyHangzhouChina
  4. 4.School of Electronics and InformationNorthwestern Polytechnical UniversityXi AnChina

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