Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation

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 di...

Descripción completa

Detalles Bibliográficos
Autores principales: Wassermann, D., Descoteaux, M., Deriche, R.
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2008
Materias:
Diffusion
Image segmentation
Magnetic resonance imaging
Neurology
Diffusion maps
Q-ball imaging segmentation
White matter fiber clustering
Medical imaging
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann
Aporte de:
Aportado por : Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires .
id paperaa:paper_16874188_v2008_n1_p_Wassermann
record_format dspace
spelling paperaa:paper_16874188_v2008_n1_p_Wassermann2023-02-10T13:37:46Z Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation Int. J. Biomed. Imaging 2008;2008(1) Wassermann, D. Descoteaux, M. Deriche, R. Diffusion Image segmentation Magnetic resonance imaging Neurology Diffusion maps Q-ball imaging segmentation White matter fiber clustering Medical imaging 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. Fil:Wassermann, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2008 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
topic Diffusion
Image segmentation
Magnetic resonance imaging
Neurology
Diffusion maps
Q-ball imaging segmentation
White matter fiber clustering
Medical imaging
spellingShingle Diffusion
Image segmentation
Magnetic resonance imaging
Neurology
Diffusion maps
Q-ball imaging segmentation
White matter fiber clustering
Medical imaging
Wassermann, D.
Descoteaux, M.
Deriche, R.
Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
topic_facet Diffusion
Image segmentation
Magnetic resonance imaging
Neurology
Diffusion maps
Q-ball imaging segmentation
White matter fiber clustering
Medical imaging
description 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.
format Artículo
Artículo
publishedVersion
author Wassermann, D.
Descoteaux, M.
Deriche, R.
author_facet Wassermann, D.
Descoteaux, M.
Deriche, R.
author_sort Wassermann, D.
title Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
title_short Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
title_full Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
title_fullStr Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
title_full_unstemmed Diffusion maps clustering for magnetic resonance Q-ball imaging segmentation
title_sort diffusion maps clustering for magnetic resonance q-ball imaging segmentation
publishDate 2008
url http://hdl.handle.net/20.500.12110/paper_16874188_v2008_n1_p_Wassermann
work_keys_str_mv AT wassermannd diffusionmapsclusteringformagneticresonanceqballimagingsegmentation
AT descoteauxm diffusionmapsclusteringformagneticresonanceqballimagingsegmentation
AT dericher diffusionmapsclusteringformagneticresonanceqballimagingsegmentation
_version_ 1759062948545822720

Ejemplares similares