C. Dumoulin and H. Hart, Magnetic resonance angiography., Radiology, vol.161, issue.3, pp.717-720, 1986.
DOI : 10.1148/radiology.161.3.3786721

F. Wehrli, A. Shimakawa, G. Gullberg, and J. Mcfall, Time-of-flight MR flow imaging: selective saturation recovery with gradient refocusing., Radiology, vol.160, issue.3, pp.781-785, 1986.
DOI : 10.1148/radiology.160.3.3526407

C. Dumoulin, S. Souza, M. Walker, and W. Wagle, Three-dimensional phase contrast angiography, Magnetic Resonance in Medicine, vol.4, issue.1, pp.139-149, 1989.
DOI : 10.1148/radiology.161.3.3786721

K. Bühler, P. Felkel, and A. L. Cruz, Geometric Methods for Vessel Visualization and Quantification ??? A Survey, 2002.
DOI : 10.1007/978-3-662-07443-5_24

P. Felkel, R. Wegenkittl, and A. Kanitsar, Vessel tracking in peripheral CTA datasets-an overview, Proceedings Spring Conference on Computer Graphics, pp.232-239, 2001.
DOI : 10.1109/SCCG.2001.945359
URL : http://www.vrvis.at/research/../TR/2001/TR_VRVis_2001_009_Full.pdf

C. Kirbas and F. Quek, A review of vessel extraction techniques and algorithms, ACM Computing Surveys, vol.36, issue.2, pp.81-121, 2004.
DOI : 10.1145/1031120.1031121

J. Suri, K. Liu, L. Reden, and S. Laxminarayan, A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II, IEEE Transactions on Information Technology in Biomedicine, vol.6, issue.4, pp.338-350, 2002.
DOI : 10.1109/TITB.2002.804136

G. Marchal, D. Vandermeulen, H. Bosmans, D. Delaere, P. Suetens et al., A threedimensional line filter for improved visualization of MRA, 1990.

G. Gerig, O. Kübler, R. Kikinis, and F. Jolesz, Nonlinear anisotropic filtering of MRI data, IEEE Transactions on Medical Imaging, vol.11, issue.2, pp.221-232, 1992.
DOI : 10.1109/42.141646

H. Chen and J. Hale, An Algorithm for MR Angiography Image Enhancement, Magnetic Resonance in Medicine, vol.9, issue.4, pp.534-540, 1995.
DOI : 10.2214/ajr.154.3.2106232

Y. Du and D. Parker, Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correlation, Journal of Magnetic Resonance Imaging, vol.28, issue.2, pp.447-450, 1997.
DOI : 10.1148/radiology.185.3.1438782

M. Orkisz, C. Bresson, I. Magnin, O. Champin, and P. Douek, Improved vessel visualization in MR angiography by nonlinear anisotropic filtering, Magnetic Resonance in Medicine, vol.28, issue.6, pp.914-919, 1997.
DOI : 10.1148/radiology.196.3.7644630

C. Westin, L. Wigström, T. Loock, L. Sjöqvist, R. Kikinis et al., Three-dimensional adaptive filtering in magnetic resonance angiography, Journal of Magnetic Resonance Imaging, vol.36, issue.1, pp.63-71, 2001.
DOI : 10.1002/mrm.1910360521
URL : http://onlinelibrary.wiley.com/doi/10.1002/jmri.1152/pdf

Y. Sun and D. Parker, Small vessel enhancement in MRA images using local maximum mean processing, IEEE Transactions on Image Processing, vol.10, issue.11, pp.1687-1699, 2001.
DOI : 10.1109/83.967397

G. Gerig, T. Koller, G. Székely, C. Brechbühler, and O. Kübler, Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data, Information Processing in Medical Imaging -IPMI'93, pp.94-111, 1993.
DOI : 10.1007/BFb0013783

N. Passat, C. Ronse, J. Baruthio, J. Armspach, and J. Foucher, Using Watershed and Multimodal Data for Vessel Segmentation: Application to the Superior Sagittal Sinus, Mathematical Morphology: 40 years on -Proceedings of the 7th International Symposium on Mathematical Morphology of Computational Imaging and Vision, pp.419-428, 2005.
DOI : 10.1007/1-4020-3443-1_38
URL : https://hal.archives-ouvertes.fr/hal-01694973

S. Kobashi, Y. Hata, Y. Tokimoto, and M. Ishikawa, Automatic segmentation of blood vessels from MR angiography volume data by using fuzzy logic technique, in: Medical Imaging: Image Processing, SPIE Proceedings, pp.968-976, 1999.

P. Yim, P. Choyke, and R. Summers, Gray-scale skeletonization of small vessels in magnetic resonance angiography, IEEE Transactions on Medical Imaging, vol.19, issue.6, pp.568-576, 2000.
DOI : 10.1109/42.870662

H. Cline, C. Dumoulin, W. Lorensen, S. Souza, and W. Adams, Volume rendering and connectivity algorithms for MR angiography, Magnetic Resonance in Medicine, vol.15, issue.2, pp.384-394, 1991.
DOI : 10.1148/radiology.173.2.2798885

U. Klose, D. Petersen, and J. Martos, Tracking of cerebral vessels in MR angiography after highpass filtering, Magnetic Resonance Imaging, vol.13, issue.1, pp.45-51, 1995.
DOI : 10.1016/0730-725X(94)00095-K

W. Lin, E. Haacke, T. Masaryk, and A. Smith, Automated local maximum-intensity projection with three-dimensional vessel tracking, Journal of Magnetic Resonance Imaging, vol.179, issue.5, pp.519-526, 1992.
DOI : 10.1148/radiology.179.3.2027996

X. Hu, N. Alperin, D. Levin, K. Tan, and M. Mengeot, Visualization of MR angiographic data with segmentation and volume-rendering techniques, Journal of Magnetic Resonance Imaging, vol.72, issue.5, pp.539-546, 1992.
DOI : 10.1148/radiology.171.1.2928539

D. Saloner, W. Hanson, J. Tsuruda, R. Van-tyen, C. Anderson et al., Application of a connected-voxel algorithm to MR angiographic data, Journal of Magnetic Resonance Imaging, vol.176, issue.4, pp.423-430, 1991.
DOI : 10.1148/radiology.176.3.2389051

O. Wink, W. Niessen, and M. Viergever, Fast delineation and visualization of vessels in 3-D angiographic images, IEEE Transactions on Medical Imaging, vol.19, issue.4, pp.337-346, 2000.
DOI : 10.1109/42.848184

H. Ehricke, K. Donner, W. Koller, and W. Strasser, Visualization of vasculature from volume data, Computers & Graphics, vol.18, issue.3, pp.395-406, 1994.
DOI : 10.1016/0097-8493(94)90040-X

N. Flasque, M. Desvignes, J. Constans, and M. Revenu, Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images, Medical Image Analysis, vol.5, issue.3, pp.173-183, 2001.
DOI : 10.1016/S1361-8415(01)00038-X
URL : https://hal.archives-ouvertes.fr/hal-00805904

C. Lorenz, I. Carlsen, T. Buzug, C. Fassnacht, and J. Weese, Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images, Lecture Notes in Computer Science, vol.1205, pp.233-242, 1997.
DOI : 10.1007/BFb0029242

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida et al., Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, Medical Image Analysis, vol.2, issue.2, pp.143-168, 1998.
DOI : 10.1016/S1361-8415(98)80009-1

K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset, Model-Based Detection of Tubular Structures in 3D Images, Computer Vision and Image Understanding, vol.80, issue.2, pp.130-171, 2000.
DOI : 10.1006/cviu.2000.0866
URL : https://hal.archives-ouvertes.fr/inria-00072929

V. Prinet, O. Monga, S. Ge, C. Xie, and S. Ma, Thin network extraction in 3D images: application to medical angiograms, Proceedings of 13th International Conference on Pattern Recognition, pp.386-390, 1996.
DOI : 10.1109/ICPR.1996.546975

S. Aylward and E. Bullit, Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction, IEEE Transactions on Medical Imaging, vol.21, issue.2, pp.61-75, 2002.
DOI : 10.1109/42.993126
URL : http://www.isi.uu.nl/Meetings/../TGV/Aylward.pdf

E. Bullitt, S. Aylward, K. Smith, M. Mukherji, K. Jiroutek et al., Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms, Medical Image Analysis, vol.5, issue.2, pp.157-169, 2001.
DOI : 10.1016/S1361-8415(01)00037-8

L. Lorigo, O. Faugeras, W. Grimson, R. Keriven, R. Kikinis et al., CURVES: Curve evolution for vessel segmentation, Medical Image Analysis, vol.5, issue.3, pp.195-206, 2001.
DOI : 10.1016/S1361-8415(01)00040-8
URL : http://www.enpc.fr/certis/publications/papers/01mia.pdf

P. Yim, J. Cebral, R. Mullick, and P. Choyke, Vessel surface reconstruction with a tubular deformable model, IEEE Transactions on Medical Imaging, vol.20, issue.12, pp.1411-1421, 2001.
DOI : 10.1109/42.974935

P. Yim, G. Boudewijn, C. Vasbiner, V. Ho, and P. Choyke, Isosurfaces as deformable models for magnetic resonance angiography, IEEE Transactions on Medical Imaging, vol.22, issue.7, pp.875-881, 2003.
DOI : 10.1109/TMI.2003.815056

A. Frangi, W. Niessen, R. Hoogeveen, T. Van-walsum, and M. Viergever, Model-based quantitation of 3-D magnetic resonance angiographic images, IEEE Transactions on Medical Imaging, vol.18, issue.10, pp.946-956, 1999.
DOI : 10.1109/42.811279

A. Chung, J. Noble, and P. Summers, Fusing speed and phase information for vascular segmentation of phase contrast MR angiograms, Medical Image Analysis, vol.6, issue.2, pp.109-128, 2002.
DOI : 10.1016/S1361-8415(02)00057-9

M. Sabry-hassouna, A. Farag, S. Hushek, and T. Moriarty, Statistical-Based Approach for Extracting 3D Blood Vessels from TOF-MyRA Data, Lecture Notes in Computer Science, vol.2878, pp.680-687, 2003.
DOI : 10.1007/978-3-540-39899-8_83

D. Wilson and J. Noble, An adaptive segmentation algorithm for time-of-flight MRA data, IEEE Transactions on Medical Imaging, vol.18, issue.10, pp.938-945, 1999.
DOI : 10.1109/42.811277

B. Avants and J. Williams, An Adaptive Minimal Path Generation Technique for Vessel Tracking in CTA/CE-MRA Volume Images, Lecture Notes in Computer Science, vol.1935, pp.707-716, 2000.
DOI : 10.1007/978-3-540-40899-4_73

S. Kobashi, N. Kamiura, Y. Hata, and F. Miyawaki, Volume-quantization-based neural network approach to 3D MR angiography image segmentation, Image and Vision Computing, vol.19, issue.4, pp.185-193, 2001.
DOI : 10.1016/S0262-8856(00)00067-6

C. Zahlten, H. Jürgens, C. Evertsz, R. Leppek, H. Peitgen et al., Portal vein reconstruction based on topology, European Journal of Radiology, vol.19, issue.2, pp.96-100, 1995.
DOI : 10.1016/0720-048X(94)00578-Z

P. Dokládal, C. Lohou, L. Perroton, and G. Bertrand, Liver Blood Vessels Extraction by a 3-D Topological Approach, Lecture Notes in Computer Science, vol.1679, pp.98-105, 1999.
DOI : 10.1007/10704282_11

A. Sanderson, D. Parker, and T. Henderson, Simultaneous segmentation of MR and X-ray angiograms for visualization of cerebral vascular anatomy, International Conference on Volume Image Processing VIP'93, pp.11-14, 1993.

I. Bloch, C. Pellot, F. Sureda, and A. Herment, 3D Reconstruction of Blood Vessels by Multi-Modality Data Fusion Using Fuzzy and Markovian Modelling, Lecture Notes in Computer Science, vol.905, pp.392-398, 1995.
DOI : 10.1007/978-3-540-49197-2_49

O. Musse, F. Heitz, and J. Armspach, Fast deformable matching of 3D images over multiscale nested subspaces. Application to atlas-based MRI segmentation, Pattern Recognition, vol.36, issue.8, pp.1881-1899, 2003.
DOI : 10.1016/S0031-3203(02)00324-2

D. Chillet, J. Jomier, D. Cool, and S. Aylward, Vascular Atlas Formation Using a Vessel-to-Image Affine Registration Method, Lecture Notes in Computer Science, vol.2878, pp.335-342, 2003.
DOI : 10.1007/978-3-540-39899-8_42
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-39899-8_42.pdf

D. Cool, D. Chillet, J. Kim, and S. Aylward, Tissue-Based Affine Registration of Brain Images to form a Vascular Density Atlas, Medical Image Computing and Computer- Assisted Intervention -MICCAI'03, pp.9-15, 2003.
DOI : 10.1007/978-3-540-39903-2_2

N. Passat, C. Ronse, J. Baruthio, J. Armspach, C. Maillot et al., Region-growing segmentation of brain vessels: An atlas-based automatic approach, Journal of Magnetic Resonance Imaging, vol.36, issue.6, pp.715-725, 2005.
DOI : 10.1148/radiology.160.3.3526407
URL : https://hal.archives-ouvertes.fr/hal-01694420

D. Chillet, N. Passat, M. Col, and J. Baruthio, Thickness Estimation of Discrete Tree-Like Tubular Objects: Application to Vessel Quantification, Scandinavian Conference on Image Analysis -SCIA'05, 14th Scandinavian Conference, Proceedings, pp.263-271, 2005.
DOI : 10.1007/11499145_28
URL : https://hal.archives-ouvertes.fr/hal-01694967

G. Bertrand, A Boolean characterization of three-dimensional simple points, Pattern Recognition Letters, vol.17, issue.2, pp.115-124, 1996.
DOI : 10.1016/0167-8655(95)00100-X
URL : https://hal.archives-ouvertes.fr/hal-00621994

V. Noblet, C. Heinrich, F. Heitz, and J. Armspach, A Topology Preserving Non-rigid Registration Method Using a Symmetric Similarity Function-Application to 3-D Brain Images, European Conference on Computer Vision -ECCV'04, pp.546-557, 2004.
DOI : 10.1007/978-3-540-24672-5_43

V. Noblet, C. Heinrich, F. Heitz, and J. Armspach, 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization, IEEE Transactions on Image Processing, vol.14, issue.5, pp.553-566, 2005.
DOI : 10.1109/TIP.2005.846026

C. Ronse, A Lattice-Theoretical Morphological View on Template Extraction in Images, Journal of Visual Communication and Image Representation, vol.7, issue.3, pp.273-295, 1996.
DOI : 10.1006/jvci.1996.0024

P. Soille, Advances in the Analysis of Topographic Features on Discrete Images, Lecture Notes in Computer Science, vol.2301, pp.176-186, 2002.
DOI : 10.1007/3-540-45986-3_16

B. Naegel, C. Ronse, and L. Soler, Using Grey Scale Hit-Or-Miss Transform for Segmenting the Portal Network of the Liver, Mathematical Morphology: 40 years on - Proceedings of the 7th International Symposium on Mathematical Morphology of Computational Imaging and Vision, pp.429-437, 2005.
DOI : 10.1007/1-4020-3443-1_39

M. Bosc, T. Vik, J. Armspach, and F. Heitz, ImLib3D: An Efficient, Open Source, Medical Image Processing Framework in C++, Medical Image Computing and Computer- Assisted Intervention -MICCAI'03, pp.981-982, 2003.
DOI : 10.1007/978-3-540-39903-2_133