Abstract
This paper proposes a method to improve brain-tissue segmentation, especially in subcortical region, by fusing the information in structural magnetic resonance (MR) images and diffusion tensor (DT) images in a sound statistical framework. The proposed method incorporates the information in DT images by parameterizing the space of diffusion tensors, in a principled and efficient manner, based on a set of independent orthogonal invariants. The proposed method couples the Markov tissue statistics of the structural-MR intensities with the tissue statistics of the DT invariants to define multivariate/joint probability density functions (PDFs) that differentiate brain tissues. The paper shows that while the information in DT images can allow improved differentiation between tissues in the subcortical region, which comprises anatomical structures having smooth (blob-like) shapes, it can produce unreliable results in the cortical regions that depict convoluted sulci/gyri. The proposed method exploits these characteristics of the images by introducing an appropriate anisotropic distance metric in the multivariate feature space.
| Original language | English (US) |
|---|---|
| Title of host publication | 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI |
| Pages | 213-216 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2008 |
| Event | 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France Duration: May 14 2008 → May 17 2008 |
Other
| Other | 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI |
|---|---|
| Country | France |
| City | Paris |
| Period | 5/14/08 → 5/17/08 |
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Keywords
- DTI
- MRI
- Multivariate statistical analysis
- Subcortical brain tissue segmentation
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Multivariate segmentation of brain tissues by fusion of MRI and DTI data. / Awate, Suyash P.; Zhang, Hui; Simon, Tony J; Gee, James C.
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI. 2008. p. 213-216 4540970.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multivariate segmentation of brain tissues by fusion of MRI and DTI data
AU - Awate, Suyash P.
AU - Zhang, Hui
AU - Simon, Tony J
AU - Gee, James C.
PY - 2008
Y1 - 2008
N2 - This paper proposes a method to improve brain-tissue segmentation, especially in subcortical region, by fusing the information in structural magnetic resonance (MR) images and diffusion tensor (DT) images in a sound statistical framework. The proposed method incorporates the information in DT images by parameterizing the space of diffusion tensors, in a principled and efficient manner, based on a set of independent orthogonal invariants. The proposed method couples the Markov tissue statistics of the structural-MR intensities with the tissue statistics of the DT invariants to define multivariate/joint probability density functions (PDFs) that differentiate brain tissues. The paper shows that while the information in DT images can allow improved differentiation between tissues in the subcortical region, which comprises anatomical structures having smooth (blob-like) shapes, it can produce unreliable results in the cortical regions that depict convoluted sulci/gyri. The proposed method exploits these characteristics of the images by introducing an appropriate anisotropic distance metric in the multivariate feature space.
AB - This paper proposes a method to improve brain-tissue segmentation, especially in subcortical region, by fusing the information in structural magnetic resonance (MR) images and diffusion tensor (DT) images in a sound statistical framework. The proposed method incorporates the information in DT images by parameterizing the space of diffusion tensors, in a principled and efficient manner, based on a set of independent orthogonal invariants. The proposed method couples the Markov tissue statistics of the structural-MR intensities with the tissue statistics of the DT invariants to define multivariate/joint probability density functions (PDFs) that differentiate brain tissues. The paper shows that while the information in DT images can allow improved differentiation between tissues in the subcortical region, which comprises anatomical structures having smooth (blob-like) shapes, it can produce unreliable results in the cortical regions that depict convoluted sulci/gyri. The proposed method exploits these characteristics of the images by introducing an appropriate anisotropic distance metric in the multivariate feature space.
KW - DTI
KW - MRI
KW - Multivariate statistical analysis
KW - Subcortical brain tissue segmentation
UR - http://www.scopus.com/inward/record.url?scp=51049121151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51049121151&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2008.4540970
DO - 10.1109/ISBI.2008.4540970
M3 - Conference contribution
AN - SCOPUS:51049121151
SN - 9781424420032
SP - 213
EP - 216
BT - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
ER -