TY - GEN
T1 - White matter fiber tract segmentation using nonnegative matrix factorization
AU - Liang, Xuwei
AU - Wang, Jie
AU - Lin, Zhenmin
AU - Zhang, Jun
PY - 2009
Y1 - 2009
N2 - Accurate and efficient white matter fiber tract segmentation is an important step in clinical and anatomical studies that use diffusion tensor magnetic resonance imaging (DTI) tractography techniques. In this work, we present a novel technique to group white matter fiber tracts reconstructed from DTI into bundles using Nonnegative Matrix Factorization (NMF) of the frequency-tract matrix. A fiber tract is quantified by Fourier descriptors in terms of frequencies. Fourier descriptors derived from the shape signature, the central angle dot product, are used to construct the nonnegative frequency-tract matrix which is analogous to the term-document matrix in the document clustering context. In the NMF derived feature space, each basis vector captures the base shape of a particular fiber tract bundle. Each fiber tract is represented as an additive combination of the base shapes. The cluster label of each fiber tract is easily determined by finding the basis vector with which a fiber tract has the largest projection value. Preliminary experimental results with real DTI data show that this method efficiently groups tracts into plausible bundles. This indicates that NMF may be used in fiber tract segmentation with appropriate fiber tract encodings.
AB - Accurate and efficient white matter fiber tract segmentation is an important step in clinical and anatomical studies that use diffusion tensor magnetic resonance imaging (DTI) tractography techniques. In this work, we present a novel technique to group white matter fiber tracts reconstructed from DTI into bundles using Nonnegative Matrix Factorization (NMF) of the frequency-tract matrix. A fiber tract is quantified by Fourier descriptors in terms of frequencies. Fourier descriptors derived from the shape signature, the central angle dot product, are used to construct the nonnegative frequency-tract matrix which is analogous to the term-document matrix in the document clustering context. In the NMF derived feature space, each basis vector captures the base shape of a particular fiber tract bundle. Each fiber tract is represented as an additive combination of the base shapes. The cluster label of each fiber tract is easily determined by finding the basis vector with which a fiber tract has the largest projection value. Preliminary experimental results with real DTI data show that this method efficiently groups tracts into plausible bundles. This indicates that NMF may be used in fiber tract segmentation with appropriate fiber tract encodings.
UR - http://www.scopus.com/inward/record.url?scp=72749105634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72749105634&partnerID=8YFLogxK
U2 - 10.1109/ICBBE.2009.5163763
DO - 10.1109/ICBBE.2009.5163763
M3 - Conference contribution
AN - SCOPUS:72749105634
SN - 9781424429028
T3 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
BT - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Y2 - 11 June 2009 through 13 June 2009
ER -