### 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.

Original language | English |
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Title of host publication | Mathematics and Visualization |

Editors | Chantal M.W. Tax, Lipeng Ning, Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband |

Publisher | Springer Heidelberg |

Pages | 69-76 |

Number of pages | 8 |

Edition | 226249 |

ISBN (Print) | 9783030058302 |

DOIs | |

Publication status | Published - 2019 |

Event | International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sep 20 → 2018 Sep 20 |

### Publication series

Name | Mathematics and Visualization |
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Number | 226249 |

ISSN (Print) | 1612-3786 |

ISSN (Electronic) | 2197-666X |

### Conference

Conference | International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 |
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Country | Spain |

City | Granada |

Period | 18/9/20 → 18/9/20 |

### Keywords

- Diffusion MRI
- Sparse NMF
- Spherical mean
- Tissue segmentation

### ASJC Scopus subject areas

- Modelling and Simulation
- Geometry and Topology
- Computer Graphics and Computer-Aided Design
- Applied Mathematics

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## Cite this

*Mathematics and Visualization*(226249 ed., pp. 69-76). (Mathematics and Visualization; No. 226249). Springer Heidelberg. https://doi.org/10.1007/978-3-030-05831-9_6