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...
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| Format: | Article |
| Language: | English |
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Wiley
2008-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2008/526906 |
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| _version_ | 1832561247876284416 |
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| author | Demian Wassermann Maxime Descoteaux Rachid Deriche |
| author_facet | Demian Wassermann Maxime Descoteaux Rachid Deriche |
| author_sort | Demian Wassermann |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-d3517c7edadd43579ad8e79077c77c4c |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-d3517c7edadd43579ad8e79077c77c4c2025-02-03T01:25:34ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962008-01-01200810.1155/2008/526906526906Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging SegmentationDemian Wassermann0Maxime Descoteaux1Rachid Deriche2Odyssee Project Team INRIA/ENPC/ENS INRIA, Sophia-Antipolis, 2004 Route des Lucioles, Sophia Antipolis 06902, FranceOdyssee Project Team INRIA/ENPC/ENS INRIA, Sophia-Antipolis, 2004 Route des Lucioles, Sophia Antipolis 06902, FranceOdyssee Project Team INRIA/ENPC/ENS INRIA, Sophia-Antipolis, 2004 Route des Lucioles, Sophia Antipolis 06902, FranceWhite 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.http://dx.doi.org/10.1155/2008/526906 |
| spellingShingle | Demian Wassermann Maxime Descoteaux Rachid Deriche Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation International Journal of Biomedical Imaging |
| title | 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_short | Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation |
| title_sort | diffusion maps clustering for magnetic resonance q ball imaging segmentation |
| url | http://dx.doi.org/10.1155/2008/526906 |
| work_keys_str_mv | AT demianwassermann diffusionmapsclusteringformagneticresonanceqballimagingsegmentation AT maximedescoteaux diffusionmapsclusteringformagneticresonanceqballimagingsegmentation AT rachidderiche diffusionmapsclusteringformagneticresonanceqballimagingsegmentation |