High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution
In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based statistical surface evolution on this image of...
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| Published in | Journal of mathematical imaging and vision Vol. 33; no. 2; pp. 239 - 252 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.02.2009
Springer Verlag |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based statistical surface evolution on this image of ODFs to efficiently find coherent white matter fiber bundles. We show that our method is appropriate to propagate through regions of fiber crossings and we show that our results outperform state-of-the-art diffusion tensor (DT) imaging segmentation methods, inherently limited by the DT model. Results obtained on synthetic data, on a biological phantom, on real datasets and on all 13 subjects of a public NMR database show that our method is reproducible, automatic and brings a strong added value to diffusion MRI segmentation. |
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| AbstractList | In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based statistical surface evolution on this image of ODFs to efficiently find coherent white matter fiber bundles. We show that our method is appropriate to propagate through regions of fiber crossings and we show that our results outperform state-of-the-art diffusion tensor (DT) imaging segmentation methods, inherently limited by the DT model. Results obtained on synthetic data, on a biological phantom, on real datasets and on all 13 subjects of a public NMR database show that our method is reproducible, automatic and brings a strong added value to diffusion MRI segmentation. |
| Author | Deriche, Rachid Descoteaux, Maxime |
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| BackLink | https://inria.hal.science/inria-00423393$$DView record in HAL |
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| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2018_07_070 crossref_primary_10_1016_j_neuroimage_2012_01_076 crossref_primary_10_1016_j_neuroimage_2022_119199 crossref_primary_10_3389_fnhum_2014_00715 crossref_primary_10_1007_s10851_019_00897_w crossref_primary_10_1016_j_neuroimage_2011_01_053 crossref_primary_10_1016_j_cmpb_2023_107630 crossref_primary_10_1007_s12021_023_09636_4 crossref_primary_10_1109_TBME_2015_2391913 crossref_primary_10_1002_nbm_3805 crossref_primary_10_1016_j_neuroimage_2008_10_054 crossref_primary_10_1002_nbm_3426 crossref_primary_10_1109_TMI_2013_2284360 crossref_primary_10_1186_1471_2342_12_10 crossref_primary_10_1109_TIP_2009_2035886 crossref_primary_10_1109_TIP_2013_2240005 |
| Cites_doi | 10.1117/1.1527628 10.1093/cercor/bhk034 10.1023/A:1020874308076 10.1002/mrm.20667 10.1006/jmrb.1996.0086 10.1002/mrm.20931 10.1088/0266-5611/19/5/303 10.1109/83.902291 10.1006/jcph.1996.0167 10.1109/TMI.2005.854516 10.1016/S1053-8119(03)00044-2 10.1016/j.neuroimage.2004.07.037 10.1016/j.neuroimage.2005.05.014 10.1002/mrm.10268 10.1016/j.neuroimage.2006.02.043 10.1109/TMI.2006.873299 10.1002/mrm.20279 10.1016/j.neuroimage.2006.01.024 10.1002/mrm.10209 10.1007/s11263-006-8711-1 10.1016/j.media.2004.07.004 10.1002/mrm.21277 10.1002/mrm.20948 10.1016/0021-9991(88)90002-2 10.1006/jvci.2001.0475 10.1007/978-3-540-24673-2_25 10.1093/oso/9780198539445.001.0001 10.1007/11505730_7 10.1007/11505730_26 10.1007/978-3-540-27816-0_11 10.1109/ISBI.2006.1624857 10.1007/11744078_36 10.1109/CVPR.2004.1315119 10.1017/CBO9781107325937 |
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| Keywords | High angular resolution diffusion imaging (HARDI) Orientation distribution function (ODF) Level set framework Q-ball imaging (QBI) Diffusion tensor imaging (DTI) Region-based segmentation |
| Language | English |
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| Snippet | In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution... |
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| SubjectTerms | Applications of Mathematics Computer Science Image Processing Image Processing and Computer Vision Mathematical Methods in Physics Signal,Image and Speech Processing |
| Title | High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution |
| URI | https://link.springer.com/article/10.1007/s10851-008-0071-8 https://inria.hal.science/inria-00423393 |
| Volume | 33 |
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