D. Kaba, A. G. Salazar-gonzalez, Y. Li, X. Liu, and A. Serag, Segmentation of Retinal Blood Vessels Using Gaussian Mixture Models and Expectation Maximisation, HIS, pp.105-112, 2013.
DOI : 10.1007/978-3-642-37899-7_9

D. L. Wilson and J. A. 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

C. Florin, N. Paragios, and J. Williams, Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries, MICCAI, pp.246-253, 2005.
DOI : 10.1007/11566465_31

K. Allen, C. Yau, and J. A. Noble, A recursive, stochastic vessel segmentation framework that robustly handles bifurcations, MIUA, 2008.

M. W. Law and A. C. Chung, Segmentation of Intracranial Vessels and Aneurysms in Phase Contrast Magnetic Resonance Angiography Using Multirange Filters and Local Variances, IEEE Transactions on Image Processing, vol.22, issue.3, pp.845-859, 2013.
DOI : 10.1109/TIP.2012.2216274

T. Chan, S. Esedoglu, and M. Nikolova, Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models, SIAM Journal on Applied Mathematics, vol.66, issue.5, pp.1632-1648, 2006.
DOI : 10.1137/040615286

J. A. Tyrrell, E. Di-tomaso, D. Fuja, R. Tong, K. Kozak et al., Robust 3-D Modeling of Vasculature Imagery Using Superellipsoids, IEEE Transactions on Medical Imaging, vol.26, issue.2, pp.223-237, 2007.
DOI : 10.1109/TMI.2006.889722

C. Tai, X. Zhang, and Z. Shen, Wavelet Frame Based Multiphase Image Segmentation, SIAM Journal on Imaging Sciences, vol.6, issue.4
DOI : 10.1137/120901751

R. Rigamonti and V. Lepetit, Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters, MICCAI, 2012, pp.189-197
DOI : 10.1007/978-3-642-33415-3_24

URL : https://infoscience.epfl.ch/record/178714/files/SM_rigamonti2012a.pdf

J. Stühmer, P. Schröder, and D. Cremers, Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.290

K. W. Sum and P. Y. Cheung, Vessel Extraction Under Non-Uniform Illumination: A Level Set Approach, IEEE Transactions on Biomedical Engineering, vol.55, issue.1, pp.358-360, 2008.
DOI : 10.1109/TBME.2007.896587

URL : http://hub.hku.hk/bitstream/10722/57473/1/143210.pdf?accept=1

F. M. 'hiri, N. L. Hoang, L. Duong, and M. Cheriet, A new adaptive framework for tubular structures segmentation in Xray angiography, ISSPA, 2012, pp.496-500

M. Holtzman-gazit, R. Kimmel, N. Peled, and D. Goldsher, Segmentation of thin structures in volumetric medical images, IEEE Transactions on Image Processing, vol.15, issue.2, pp.354-363, 2006.
DOI : 10.1109/TIP.2005.860624

J. Mille and L. D. Cohen, 3D CTA image segmentation with a generalized cylinder-based tree model, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1045-1048, 2010.
DOI : 10.1109/ISBI.2010.5490169

URL : https://hal.archives-ouvertes.fr/hal-00701396

A. Gooya, H. Liao, K. Matsumiya, K. Masamune, and T. Dohi, Effective Statistical Edge Integration Using a Flux Maximizing Scheme for Volumetric Vascular Segmentation in MRA, IPMI, pp.86-97, 2007.
DOI : 10.1007/978-3-540-73273-0_8

N. Y. El-zehiry and L. Grady, Vessel segmentation using 3D elastica regularization, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.1288-1291
DOI : 10.1109/ISBI.2012.6235798

URL : http://www.cns.bu.edu/%7Elgrady/elzehiry2012vessel.pdf

S. Lankton and A. Tannenbaum, Localizing Region-Based Active Contours, IEEE Transactions on Image Processing, vol.17, issue.11, pp.2029-2039, 2008.
DOI : 10.1109/TIP.2008.2004611

B. Caldairou, N. Passat, P. Habas, C. Studholme, and F. Rousseau, A non-local fuzzy segmentation method: Application to brain MRI, Pattern Recognition, vol.44, issue.9, pp.1916-1927, 2011.
DOI : 10.1016/j.patcog.2010.06.006

URL : https://hal.archives-ouvertes.fr/hal-01695014

Y. Wang, Y. Xiong, L. Lv, H. Zhang, Z. Cao et al., Vector-valued Chan-Vese model driven by local histogram for texture segmentation, 2010 IEEE International Conference on Image Processing, pp.645-648, 2010.
DOI : 10.1109/ICIP.2010.5651442

C. Darolti, A. Mertins, C. Bodensteiner, and U. G. Hofmann, Local Region Descriptors for Active Contours Evolution, IEEE Transactions on Image Processing, vol.17, issue.12, pp.2275-2288, 2008.
DOI : 10.1109/TIP.2008.2006443

URL : http://www.isip.uni-luebeck.de/uploads/tx_wapublications/cristina-TIP2008.pdf

E. S. Brown, T. F. Chan, and X. Bresson, Completely Convex Formulation of the Chan-Vese Image Segmentation Model, International Journal of Computer Vision, vol.26, issue.2, pp.103-121, 2012.
DOI : 10.1007/BF02592050

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

H. H. Bauschke and P. L. , Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2011.

P. L. Combettes and J. Pesquet, Proximal Splitting Methods in Signal Processing, Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2011.
DOI : 10.1007/978-1-4419-9569-8_10

URL : https://hal.archives-ouvertes.fr/hal-00643807

P. L. Combettes and J. Pesquet, Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators, Set-Valued and Variational Analysis, vol.38, issue.2, pp.307-330, 2012.
DOI : 10.1142/9789812777096

URL : https://hal.archives-ouvertes.fr/hal-00794044

S. Lefkimmiatis, A. Bourquard, and M. Unser, Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications, IEEE Transactions on Image Processing, vol.21, issue.3, pp.983-995, 2012.
DOI : 10.1109/TIP.2011.2168232

URL : http://bigwww.epfl.ch/publications/lefkimmiatis1201.pdf

J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. Van-ginneken, Ridge-Based Vessel Segmentation in Color Images of the Retina, IEEE Transactions on Medical Imaging, vol.23, issue.4, pp.501-509, 2004.
DOI : 10.1109/TMI.2004.825627

G. Hamarneh and P. Jassi, VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis, Computerized Medical Imaging and Graphics, vol.34, issue.8, pp.605-616, 2010.
DOI : 10.1016/j.compmedimag.2010.06.002

URL : http://www.cs.sfu.ca/~hamarneh/ecopy/cmig2010.pdf

M. E. Martínez-pérez, A. D. Hughes, A. V. Stanton, S. A. Thom, A. A. Bharath et al., Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing, MICCAI, pp.90-97, 1999.
DOI : 10.1007/10704282_10

M. M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit et al., An approach to localize the retinal blood vessels using bit planes and centerline detection, Computer Methods and Programs in Biomedicine, vol.108, issue.2, pp.600-616, 2012.
DOI : 10.1016/j.cmpb.2011.08.009

M. Niemeijer, J. J. Staal, B. Van-ginneken, M. Loog, and M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, Medical Imaging 2004: Image Processing, pp.648-656, 2004.
DOI : 10.1117/12.535349