Diffusion Tensor Estimation, Regularization and Classification

In this chapter, we explore diffusion tensor estimation, regularization and classification. To this end, we introduce a variational method for joint estimation and regularization of diffusion tensor fields from noisy raw data as well as a Support Vector Machine (SVM) based classification framework.

[1]  Nikos Komodakis,et al.  Fast, Approximately Optimal Solutions for Single and Dynamic MRFs , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  M. Ladd,et al.  Diffusive sensitivity to muscle architecture: a magnetic resonance diffusion tensor imaging study of the human calf , 2004, European Journal of Applied Physiology.

[3]  Ross T. Whitaker,et al.  Rician Noise Removal in Diffusion Tensor MRI , 2006, MICCAI.

[4]  Nicholas Ayache,et al.  Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[5]  E. Bullmore,et al.  Formal characterization and extension of the linearized diffusion tensor model , 2005, Human brain mapping.

[6]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[7]  Gunnar Rätsch,et al.  Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection , 2004, J. Mach. Learn. Res..

[8]  Jean-Philippe Tarel,et al.  Non-Mercer Kernels for SVM Object Recognition , 2004, BMVC.

[9]  Zhizhou Wang,et al.  A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI , 2004, IEEE Transactions on Medical Imaging.

[10]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[11]  Siwei Lyu,et al.  Mercer kernels for object recognition with local features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[13]  Carl-Fredrik Westin,et al.  3D Bayesian Regularization of Diffusion Tensor MRI Using Multivariate Gaussian Markov Random Fields , 2004, MICCAI.

[14]  John D. Lafferty,et al.  Diffusion Kernels on Statistical Manifolds , 2005, J. Mach. Learn. Res..

[15]  Simon R. Arridge,et al.  Diffusion tensor magnetic resonance image regularization , 2004, Medical Image Anal..

[16]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[17]  Rachid Deriche,et al.  Variational Approaches to the Estimation, Regularizatinn and Segmentation of Diffusion Tensor Images , 2006, Handbook of Mathematical Models in Computer Vision.

[18]  J. Hiriart-Urruty,et al.  Fundamentals of Convex Analysis , 2004 .

[19]  Christos Davatzikos,et al.  Kernel-Based Manifold Learning for Statistical Analysis of Diffusion Tensor Images , 2007, IPMI.

[20]  M. Ladd,et al.  A diffusion tensor imaging analysis of gender differences in water diffusivity within human skeletal muscle , 2005, NMR in biomedicine.

[21]  Rachid Deriche,et al.  A Riemannian approach to anisotropic filtering of tensor fields , 2007, Signal Process..

[22]  Carl-Fredrik Westin,et al.  Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas , 2007, IEEE Transactions on Medical Imaging.

[23]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[24]  Nikos Paragios,et al.  Variable Bandwidth Image Denoising Using Image-based Noise Models , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Thomas Brox,et al.  PDEs for Tensor Image Processing , 2006, Visualization and Processing of Tensor Fields.

[26]  Tony Jebara,et al.  Probability Product Kernels , 2004, J. Mach. Learn. Res..

[27]  W. Eric L. Grimson,et al.  Statistical modeling and EM clustering of white matter fiber tracts , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[28]  Frans Vos,et al.  LINEAR AND KERNEL FISHER DISCRIMINANT ANALYSIS FOR STUDYING DIFFUSION TENSOR IMAGES IN SCHIZOPHRENIA , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.