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Wassermann, D.; Descoteaux, M.; Deriche, R. "Diffusion maps segmentation of magnetic resonance Q-ball imaging" (2007) 2007 IEEE 11th International Conference on Computer Vision, ICCV
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Abstract:

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.

Registro:

Documento: Conferencia
Título:Diffusion maps segmentation of magnetic resonance Q-ball imaging
Autor:Wassermann, D.; Descoteaux, M.; Deriche, R.
Ciudad:Rio de Janeiro
Filiación:Odyssée Project Team INRIA/ENPC/ENS, INRIA, Sophia-Antipolis, 2004 Route des Lucioles, 06902 Sophia Antipolis, France
Computer Science Department, School of Exact and Natural Sciences, University of Buenos Aires, C1428EGA Buenos Aires, Argentina
Palabras clave: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
Año:2007
DOI: http://dx.doi.org/10.1109/ICCV.2007.4409166
Título revista:2007 IEEE 11th International Conference on Computer Vision, ICCV
Título revista abreviado:Proc IEEE Int Conf Comput Vision
CODEN:PICVE
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS22543_v_n_p_Wassermann

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Citas:

---------- APA ----------
Wassermann, D., Descoteaux, M. & Deriche, R. (2007) . Diffusion maps segmentation of magnetic resonance Q-ball imaging. 2007 IEEE 11th International Conference on Computer Vision, ICCV.
http://dx.doi.org/10.1109/ICCV.2007.4409166
---------- CHICAGO ----------
Wassermann, D., Descoteaux, M., Deriche, R. "Diffusion maps segmentation of magnetic resonance Q-ball imaging" . 2007 IEEE 11th International Conference on Computer Vision, ICCV (2007).
http://dx.doi.org/10.1109/ICCV.2007.4409166
---------- MLA ----------
Wassermann, D., Descoteaux, M., Deriche, R. "Diffusion maps segmentation of magnetic resonance Q-ball imaging" . 2007 IEEE 11th International Conference on Computer Vision, ICCV, 2007.
http://dx.doi.org/10.1109/ICCV.2007.4409166
---------- VANCOUVER ----------
Wassermann, D., Descoteaux, M., Deriche, R. Diffusion maps segmentation of magnetic resonance Q-ball imaging. Proc IEEE Int Conf Comput Vision. 2007.
http://dx.doi.org/10.1109/ICCV.2007.4409166