Diffusion maps segmentation of magnetic resonance Q-ball imaging

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 (...

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Detalles Bibliográficos
Publicado:	2007
Materias:	Artificial intelligence Chlorine compounds Cluster analysis Computer networks Computer vision Crossings (pipe and cable) Distribution functions Fibers Flow of solids Harmonic analysis Image processing Image segmentation Maps Medical imaging Optical projectors Population statistics Resonance Tensors Fiber bundles Number of clusters Diffusion
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS22543_v_n_p_Wassermann http://hdl.handle.net/20.500.12110/paper_NIS22543_v_n_p_Wassermann
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_NIS22543_v_n_p_Wassermann
record_format	dspace
spelling	paper:paper_NIS22543_v_n_p_Wassermann2023-06-08T16:40:00Z Diffusion maps segmentation of magnetic resonance Q-ball imaging Artificial intelligence Chlorine compounds Cluster analysis Computer networks Computer vision Crossings (pipe and cable) Distribution functions Fibers Flow of solids Harmonic analysis Image processing Image segmentation Maps Medical imaging Optical projectors Population statistics Resonance Tensors Fiber bundles Number of clusters Diffusion 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 has Q-Ball Imaging (QBI) have been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maximum(a) agreeing with the underlying fiber population. In this paper, we use the ODF representation in a small set of spherical harmonic coefficients 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 adaptative 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 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 successfully segment the fiber bundles. ©2007 IEEE. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS22543_v_n_p_Wassermann http://hdl.handle.net/20.500.12110/paper_NIS22543_v_n_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)
topic	Artificial intelligence Chlorine compounds Cluster analysis Computer networks Computer vision Crossings (pipe and cable) Distribution functions Fibers Flow of solids Harmonic analysis Image processing Image segmentation Maps Medical imaging Optical projectors Population statistics Resonance Tensors Fiber bundles Number of clusters Diffusion
spellingShingle	Artificial intelligence Chlorine compounds Cluster analysis Computer networks Computer vision Crossings (pipe and cable) Distribution functions Fibers Flow of solids Harmonic analysis Image processing Image segmentation Maps Medical imaging Optical projectors Population statistics Resonance Tensors Fiber bundles Number of clusters Diffusion Diffusion maps segmentation of magnetic resonance Q-ball imaging
topic_facet	Artificial intelligence Chlorine compounds Cluster analysis Computer networks Computer vision Crossings (pipe and cable) Distribution functions Fibers Flow of solids Harmonic analysis Image processing Image segmentation Maps Medical imaging Optical projectors Population statistics Resonance Tensors Fiber bundles Number of clusters Diffusion
description	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 has Q-Ball Imaging (QBI) have been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maximum(a) agreeing with the underlying fiber population. In this paper, we use the ODF representation in a small set of spherical harmonic coefficients 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 adaptative 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 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 successfully segment the fiber bundles. ©2007 IEEE.
title	Diffusion maps segmentation of magnetic resonance Q-ball imaging
title_short	Diffusion maps segmentation of magnetic resonance Q-ball imaging
title_full	Diffusion maps segmentation of magnetic resonance Q-ball imaging
title_fullStr	Diffusion maps segmentation of magnetic resonance Q-ball imaging
title_full_unstemmed	Diffusion maps segmentation of magnetic resonance Q-ball imaging
title_sort	diffusion maps segmentation of magnetic resonance q-ball imaging
publishDate	2007
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS22543_v_n_p_Wassermann http://hdl.handle.net/20.500.12110/paper_NIS22543_v_n_p_Wassermann
_version_	1769175851998380032

Diffusion maps segmentation of magnetic resonance Q-ball imaging

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