5 Susumu M, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron, 2006, 51: 527-539
[6]
6 Wang Z Z, Vemuri B C. Tensor field segmentation using region based active contour model. In: Proceedings of European Conference on Computer Vision. Prague, Czech Republic, 2004. 304-315
[7]
7 Wang Z Z, Vemuri B C. An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation. In: Proceedings of IEEE Computer Society Conference Computation Vision and Pattern Recognition. Washington D C, USA, 2004. 228-233
[8]
8 Wang Z Z, Vemuri B C. DTI segmentation using an information theoretic tensor dissimilarity measure. IEEE Trans Med Imaging, 2005, 24: 1267-1277
[9]
14 Awate S, Gee J. A fuzzy, nonparametric segmentation framework for DTI and MRI analysis. In: Proceedings of Information Processing in Medical Imaging. Kerkrade, The Netherlands, 2007. 296-307
[10]
16 Nazem-Zadeh M R, Davoodi-Bojd E, Soltanian-Zadeh H. Atlas-based fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients. NeuroImage, 2011, 54: s146-s164
[11]
20 Davoodi-Bojd E, Nazem-Zadeh M R, Soltanian-Zadeh H. Atlas based segmentation of white matter fiber bundles using ODF data in reduced position orientation space (RPOS). In: Proceedings of the 6th Annual World Congress for Brain Mapping and Image Guided Therapy (Annual Congress of the IBMISPS). Boston, USA, 2009. 26-29
[12]
21 Tournier J D, Calamante F, Gadian D G, et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23: 1176-1185
[13]
22 Basser P, Pajevic S, Pierpaoli C, et al. In vivo fiber tractography using DT-MRI data. Magn Reson Med, 2000, 44: 625-632
[14]
23 Parker G, Wheeler-Kingshott C, Barker G. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans Med Imaging, 2002, 21: 505-512
[15]
24 Behrens T, Berg H J, Jbabdi S, et al. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 2007, 34: 144-155
27 Wu X, Xie M Y, Zhou J L, et al. Globally optimized fiber tracking and hierarchical clustering—A unified framework. Magn Reson Imaging, 2012, 30: 485-495
[19]
28 Li L J, Ma N, Li Z X, et al. Prefrontal white matter abnormalities in young adult with major depressive disorder: A diffusion tensor imaging study. Brain Res, 2007, 1168: 124-128
[20]
29 Yu C S, Shu N, Li J, et al. Plasticity of the corticospinal tract in early blindness revealed by quantitative analysis of fractional anisotropy based on diffusion tensor tractography. NeuroImage, 2007, 36: 411-417
[21]
32 Shu N, Li J, Li K C, et al. Abnormal diffusion of cerebral white matter in early blindness. Human Brain Mapping, 2009, 30: 220-227
[22]
33 Zhang T, Guo L, Li K M, et al. Predicting functional brain ROIs via fiber shape models In: Proceedings of MICCAI. Toronto, Canada, 2011. 42-49
[23]
34 Wang Y, Gupta A, Liu Z X, et al. DTI registration in atlas based fiber analysis of infantile Krabbe disease. NeuroImage, 2011, 55: 1577-1586
[24]
35 Weickert J, Hagen H. Visualization and Processing of Tensor Fields. Berlin: Springer, 2006. 345-412
37 Aja-Fernández S, Luis R D, Tao D C. Tensors in Image Processing and Computer Vision. London: Springer, 2009. 35-58
[27]
38 Laidlaw D, Weickert J. Visualization and Processing of Tensor Fields: Advances and Perspectives. Berlin: Springer, 2009. 113-138
[28]
39 Bougias C, Tripoliti E E. Theory of diffusion tensor imaging and fiber tractography analysis. Eur J Radiogr, 2009, 1: 37-41
[29]
40 Arsigny V, Fillard P, Pennec X, et al. Fast and simple calculus on tensors in the Log-Euclidean framework. In: Proceedings of MICCAI. California, USA, 2005. 115-122
[30]
42 Zhukov L, Museth K, Breen D, et al. Level set modeling and segmentation of DT-MRI brain data. J Electr Imaging, 2003, 12: 125-133
[31]
43 Pennec X, Fillard P, Ayache N. A Riemannian framework for tensor computing. Int J Comput Vis, 2006, 66: 41-66
[32]
44 Peeters T H J M, Rodrigues P R, Vilanova A, et al. Analysis of distance/similarity measures for diffusion tensor imaging. In: Visualization and Processing of Tensor Fields: Advances and Perspectives. Berlin: Springer, 2009. 113-136
[33]
45 Feddern C, Weickert J, Burgeth B. Level-set methods for tensor-valued images. In: Proceedings of IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision. Nice, France, 2003. 65-72
[34]
47 Duan Y, Li X, Xi Y. Thalamus segmentation from diffusion tensor magnetic resonance imaging. Int J Biomed Imaging, 2007, 2: 1-5
[35]
50 Boykov Y Y, Funka-Lea G. Graph cuts and efficient N-D Image segmentation. Int J Comput Vis, 2006, 70: 109-131
[36]
53 Rittner L, Lotufo R, Campbell J, et al. Segmentation of thalamic nuclei based on tensorial morphological gradient of diffusion tensor fields. In: Proceedings of IEEE International Symposium on Biomedical Imaging. Boston, USA, 2010. 1173-1176
[37]
54 Rittner L, Lotufo R. Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient. In: Proceedings of the XXI Brazilian Symposium on Computer Graphics and Image Processing. Campo Grande, Brazil, 2008. 196-203
[38]
55 Rittner L, Appenzeller S, Lotufo R. Segmentation of brain structures by watershed transform on tensorial morphological gradient of diffusion tensor imaging. In: Proceedings of the XXII Brazilian Symposium on Computer Graphics and Image Processing. Rio de Janeiro, Brazil, 2009. 126-132
[39]
56 Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intel, 2000, 22: 888-905
[40]
59 Malcolm J, Rathi Y, Tannenbaum A. A graph cut approach to image segmentation on tensor space. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minnesota, USA, 2007. 1-8
[41]
60 Han D, Singh V, Lee J E, et al. An experimental evaluation of diffusion tensor image segmentation using graph-cuts. In: Proceedings of the IEEE Engineering in Medicine and Biology Society (EMBC). Minnesota, USA, 2009. 5653-5656
[42]
61 Weldeselassie Y T, Hamarneh G. DT-MRI segmentation using graph cuts. In: Proceedings of SPIE. San Diego, USA, 2007. 65121K
[43]
62 Aubert G, Kormprobst P. Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Beijing: World Publishing Corporation, 2009. 207-342
[44]
63 Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational-problems. Commun Pure Appl Math, 1989, 42: 577-685
[45]
9 Lenglet C, Rousson M, Deriche R, et al. A Riemannian approach to diffusion tensor images segmentation. In: Proceedings of Information Processing in Medical Imaging. Colorado, USA, 2005. 591-602
[46]
10 Lenglet C, Rousson M, Deriche R, et al. Statistics on the manifold of multivariate normal distributions: Theory and application to diffusion tensor MRI processing. J Math Imaging Vis, 2006, 25: 423-444
[47]
11 Lenglet C, Rousson M, Deriche R, et al. A statistical framework for DTI segmentation. In: Proceedings of 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Arlington, Virginia, USA, 2006. 794-797
[48]
12 Jonasson L, Bresson X, Hagmann P, et al. White matter fiber tract segmentation in DT-MRI using geometric flows. Med Image Anal, 2005, 9: 223-236
[49]
13 Jonasson L, Hagmann P, Pollo C, et al. A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Proc, 2007, 87: 309-321
[50]
15 Awate S, Zhang H, Gee J. A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI-tract extraction. IEEE Trans Med Imaging, 2007, 26: 1525-1536
[51]
17 Li H, Zhong X, Guo L, et al. A hybrid approach to automatic clustering of white matter fibers. NeuroImage, 2010, 49: 1249-1258
[52]
18 Wassermann D, Bloy L, Kanterakis E, et al. Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers. NeuroImage, 2010, 51: 228-241
[53]
19 Eckstein I, Shattuck D W, Stein J L, et al. Active fibers: Matching deformable tract templates to diffusion tensor images. NeuroImage, 2009, 47: T82-T89
[54]
30 Jiang T Z, Liu Y, Shi F, et al. Multimodal magnetic resonance imaging for brain disorders: Advances and perspectives. Brain Imaging Behav, 2008, 2: 249-257
[55]
31 Zhou Y, Shu N, Liu Y, et al. Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophr Res, 2008, 100: 120-132
[56]
41 Arsigny V, Fillard P, Pennec X, et al. Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn Reson Med, 2006, 56: 411-421
[57]
46 Wiegell M R, Tuch D S, Larson H B W, et al. Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. NeuroImage, 2003, 19: 391-402
[58]
48 Guo W H, Chen Y M, Zeng Q G. A geometric flow-based approach for diffusion tensor image segmentation. Phil Trans Royal Soc A, 2008, 366: 2279-2291
[59]
49 Luis R D, Westin C F, Alberola C. Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging. Comput Med Imaging Graph, 2011, 35: 16-30
[60]
51 Ziyan U, Tuch D, Westin C F. Segmentation of thalamic nuclei from DTI using spectral clustering. In: Proceedings of MICCAI. Copenhagen, Denmark, 2006. 807-814
[61]
52 Luis R D, Alberola C. Mixtures of gaussians on tensor fields for DT-MRI segmentation. In: Proceedings of MICCAI. Brisbane, Australia, 2007. 319-326
[62]
57 Boykov Y Y, Jolly M P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of International Conference on Computer Vision. Vancouver, Canada, 2001. 105-112
[63]
58 Boykov Y Y, Veksler O. Graph cuts in vision and graphics: Theories and applications. In: Handbook of Mathematical Models in Computer Vision. Berlin: Springer, 2006. 79-96
[64]
64 Angelos B, Vemuri B C, Shepherd T M, et al. Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi. IEEE Trans Med Imaging, 2007, 26: 1537-1546
[65]
65 Marroquín J L, Velasco F A, Rivera M, et al. Gauss-Markov measure field models for low-level vision. IEEE Trans Pattern Anal Mach Intel, 2001, 23: 337-348
[66]
66 Zimmerman-Moreno G, Mayer A, Greenspan H. Classification trees for fast segmentation of DTI brain fiber tracts. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Alaska, USA, 2008. 1-7
[67]
67 Davoodi-Bojd E, Soltanian-Zadeh H. Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Paris, France, 2008. 879-882
[68]
68 Hasan K M, Walimuni I S, Abid H, et al. A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput Biol Med, 2011, 41: 1062-1072
