Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation
Joint Authors
Deriche, Rachid
Wassermann, Demian
Descoteaux, Maxime
Source
International Journal of Biomedical Imaging
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-02-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems.
In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles.
It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations.
QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations.
In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method.
We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps.
In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter.
We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings.
On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters.
Finally, we show results on a real-brain dataset where we segment well-known fiber bundles.
American Psychological Association (APA)
Wassermann, Demian& Descoteaux, Maxime& Deriche, Rachid. 2008. Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation. International Journal of Biomedical Imaging،Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-478738
Modern Language Association (MLA)
Wassermann, Demian…[et al.]. Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation. International Journal of Biomedical Imaging No. 2008 (2008), pp.1-12.
https://search.emarefa.net/detail/BIM-478738
American Medical Association (AMA)
Wassermann, Demian& Descoteaux, Maxime& Deriche, Rachid. Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation. International Journal of Biomedical Imaging. 2008. Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-478738
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478738