@inproceedings{e44aca44f6664c24b84e056950eba869,
title = "Tissue Segmentation Using Sparse Non-negative Matrix Factorization of{\^A} Spherical Mean Diffusion MRI Data",
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 = "Diffusion MRI, Sparse NMF, Spherical mean, Tissue segmentation",
author = "Peng Sun and Ye Wu and Geng Chen and Jun Wu and Dinggang Shen and Yap, {Pew Thian}",
year = "2019",
doi = "10.1007/978-3-030-05831-9_6",
language = "English",
isbn = "9783030058302",
series = "Mathematics and Visualization",
publisher = "Springer Heidelberg",
number = "226249",
pages = "69--76",
editor = "Tax, {Chantal M.W.} and Lipeng Ning and Elisenda Bonet-Carne and Francesco Grussu and Farshid Sepehrband",
booktitle = "Mathematics and Visualization",
edition = "226249",
note = "International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",
}