TY - GEN
T1 - A kernel-based graphical model for diffusion tensor registration
AU - Sotiras, A.
AU - Neji, R.
AU - Deux, J. F.
AU - Komodakis, N.
AU - Fleury, G.
AU - Paragios, N.
PY - 2010
Y1 - 2010
N2 - In this paper, we propose a novel method for the spatial normalization of diffusion tensor images. The proposed method takes advantage of both the diffusion information and the spatial location of tensor in order to define an appropriate metric in a probabilistic framework. A registration energy is defined in a Reproducing Kernel Hilbert Space (RKHS), encoding the image dissimilarity and the regularity of the deformation field in both the translation and the rotation space. The problem is reformulated as a graphical model where the latent variables are the rotation and the translation that should be applied to every tensor and the observed variables are the tensors themselves. Efficient linear programming is used to minimize the resulting energy. Quantitative and qualitative results on a manually annotated dataset of diffusion tensor images demonstrate the potential of the proposed method.
AB - In this paper, we propose a novel method for the spatial normalization of diffusion tensor images. The proposed method takes advantage of both the diffusion information and the spatial location of tensor in order to define an appropriate metric in a probabilistic framework. A registration energy is defined in a Reproducing Kernel Hilbert Space (RKHS), encoding the image dissimilarity and the regularity of the deformation field in both the translation and the rotation space. The problem is reformulated as a graphical model where the latent variables are the rotation and the translation that should be applied to every tensor and the observed variables are the tensors themselves. Efficient linear programming is used to minimize the resulting energy. Quantitative and qualitative results on a manually annotated dataset of diffusion tensor images demonstrate the potential of the proposed method.
KW - Diffusion tensor imaging
KW - Discrete optimization
KW - Kernels
KW - Markov random fields
KW - Spatial normalization
UR - http://www.scopus.com/inward/record.url?scp=77955181670&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2010.5490295
DO - 10.1109/ISBI.2010.5490295
M3 - Conference contribution
AN - SCOPUS:77955181670
SN - 9781424441266
T3 - 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings
SP - 524
EP - 527
BT - 2010 7th IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Y2 - 14 April 2010 through 17 April 2010
ER -