### 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 |
---|---|

Pages (from-to) | 69-76 |

Number of pages | 8 |

Journal | Mathematics and Visualization |

Issue number | 226249 |

DOIs | |

Publication status | Published - 2019 Jan 1 |

Externally published | Yes |

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 |

### Fingerprint

### 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

### Cite this

*Mathematics and Visualization*, (226249), 69-76. https://doi.org/10.1007/978-3-030-05831-9_6

**Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data.** / Sun, Peng; Wu, Ye; Chen, Geng; Wu, Jun; Shen, Dinggang; Yap, Pew Thian.

Research output: Contribution to journal › Conference article

*Mathematics and Visualization*, no. 226249, pp. 69-76. https://doi.org/10.1007/978-3-030-05831-9_6

}

TY - JOUR

T1 - Tissue Segmentation Using Sparse Non-negative Matrix Factorization ofÂ Spherical Mean Diffusion MRI Data

AU - Sun, Peng

AU - Wu, Ye

AU - Chen, Geng

AU - Wu, Jun

AU - Shen, Dinggang

AU - Yap, Pew Thian

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Diffusion MRI

KW - Sparse NMF

KW - Spherical mean

KW - Tissue segmentation

UR - http://www.scopus.com/inward/record.url?scp=85066880886&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066880886&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-05831-9_6

DO - 10.1007/978-3-030-05831-9_6

M3 - Conference article

SP - 69

EP - 76

JO - Mathematics and Visualization

JF - Mathematics and Visualization

SN - 1612-3786

IS - 226249

ER -