Medinria diffusion weighted imaging1/29/2024 ![]() Melonakos, J., et al.: Locally-constrained region-based methods for DW-MRI segmentation. In: Information Processing in Medical Imaging (2005) Lenglet, C., et al.: A Riemannian approach to diffusion tensor images segmentation. In: Larsen, R., Nielsen, M., Sporring, J. Ziyan, U., Tuch, D., Westin, C.F.: Segmentation of thalamic nuclei from DTI using spectral clustering. Jonasson, L., et al.: A level set method for segmentation of the thalamus and its nuclei in DT-MRI. In: European Conference on Computer Vision, pp. Wang, Z., Vemuri, B.: Tensor field segmentation using region based active contour model. Wiegell, M., et al.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. ![]() Perrin, M., et al.: Fiber tracking in q-ball fields using regularized particle trajectories. Springer, Heidelberg (2004)įriman, O., Farneback, G., Westin, C.F.: A Bayesian approach for stochastic white matter tractography. In: Barillot, C., Haynor, D.R., Hellier, P. Magnetic Resonance in Medicine 49, 716–721 (2003)īrun, A., et al.: Clustering fiber tracts using normalized cuts. Journal of Electronic Imaging, 125–133 (2003)ĭing, Z., et al.: Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Zhukov, L., et al.: Level set segmentation and modeling of DT-MRI human brain data. Kindlmann, G., et al.: Geodesic-loxodromes for diffusion tensor interpolation and difference measurement. Wang, Z., Vemuri, B.C.: DTI segmentation using an information theoretic tensor dissimilarity measure. International Journal of Computer Vision 66, 41–46 (2006) Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Magnetic Resonance in Medicine 56, 411–421 (2006)įletcher, P.T., Joshi, S.: Riemannian geometry for the statistical analysis of diffusion tensor data. This process is experimental and the keywords may be updated as the learning algorithm improves.Īrsigny, V., et al.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. These keywords were added by machine and not by the authors. Results on synthetic and real diffusion tensor images are also presented. Our method is computationally simple, can handle large deformations of the principal direction along the fiber tracts, and performs automatic segmentation without requiring previous fiber tracking. Then, under the assumption that different fiber bundles are physically distinct, we show that the null space of a matrix built from the local representation gives the segmentation of the fiber bundles. Such a generalization exploits geometric properties of the space of symmetric positive semi-definite matrices, particularly its Riemannian metric. We first learn a local representation of the diffusion tensor data using a generalization of the locally linear embedding (LLE) algorithm from Euclidean to diffusion tensor data. We cast this problem as a manifold clustering problem in which different fiber bundles correspond to different submanifolds of the space of diffusion tensors. We consider the problem of segmenting fiber bundles in diffusion tensor images.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |