A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, Multiscale vessel enhancement filtering, MICCAI, Proceedings, pp.130-137, 1998.
DOI : 10.1148/radiology.191.1.8134563

O. Merveille, H. Talbot, L. Najman, and N. Passat, Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.2, pp.304-317, 2018.
DOI : 10.1109/TPAMI.2017.2672972

URL : https://hal.archives-ouvertes.fr/hal-01262728

O. Merveille, O. Miraucourt, S. Salmon, N. Passat, and H. Talbot, A variational model for thin structure segmentation based on a directional regularization, 2016 IEEE International Conference on Image Processing (ICIP), pp.4324-4328, 2016.
DOI : 10.1109/ICIP.2016.7533176

URL : https://hal.archives-ouvertes.fr/hal-01472645

H. Talbot, Oriented patterns in image analysis: From thin objects to flow-based methods, 2013.
URL : https://hal.archives-ouvertes.fr/tel-01099256

D. Lesage, E. Angelini, I. Bloch, and G. Funka-lea, A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes, Medical Image Analysis, vol.13, issue.6, pp.819-845, 2009.
DOI : 10.1016/j.media.2009.07.011

Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig et al., 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, CVRMed-MRCAS, Proceedings, pp.213-222, 1997.
DOI : 10.1007/BFb0029240

URL : http://perso.telecom-paristech.fr/~bloch/P6/PRREC/linefilter.pdf

C. Lorenz, I. Carlsen, T. M. 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, CVRMed-MRCAS, Proceedings, pp.233-242, 1997.
DOI : 10.1007/BFb0029242

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

P. Danielsson, Q. Lin, and Q. Ye, Efficient Detection of Second-Degree Variations in 2D and 3D Images, Journal of Visual Communication and Image Representation, vol.12, issue.3, pp.255-305, 2001.
DOI : 10.1006/jvci.2000.0472

URL : http://liu.diva-portal.org/smash/get/diva2:241538/FULLTEXT01

W. T. Freeman and E. H. Adelson, The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.9, pp.891-906, 1991.
DOI : 10.1109/34.93808

URL : http://www.isi.uu.nl/Meetings/../TGV/steerable.pdf

K. G. Derpanis and J. M. Gryn, Three-dimensional nth derivative of Gaussian separable steerable filters, IEEE International Conference on Image Processing 2005, pp.553-556, 2005.
DOI : 10.1109/ICIP.2005.1530451

URL : http://www.cvr.yorku.ca/members/gradstudents/kosta/publications/file_ICIP05.pdf

L. Cohen and T. Deschamps, Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging, Computer Methods in Biomechanics and Biomedical Engineering, vol.21, issue.4, pp.289-305, 2007.
DOI : 10.1109/42.563665

URL : https://hal.archives-ouvertes.fr/hal-00691873

Y. Rouchdy and L. D. Cohen, Geodesic voting for the automatic extraction of tree structures. Methods and applications, Computer Vision and Image Understanding, vol.117, issue.10, pp.1453-1467, 2013.
DOI : 10.1016/j.cviu.2013.06.001

URL : https://hal.archives-ouvertes.fr/hal-00906240

L. Vincent, Minimal path algorithms for the robust detection of linear features in gray images, ISMM, Proceedings, ser. Computational Imaging and Vision, pp.331-338, 1998.

H. J. Heijmans, M. Buckley, and H. Talbot, Path Openings and Closings, Journal of Mathematical Imaging and Vision, vol.17, issue.11, pp.3-107, 2005.
DOI : 10.1007/s10851-005-4885-3

URL : https://hal.archives-ouvertes.fr/hal-00622079

C. L. Hendriks, Constrained and Dimensionality-Independent Path Openings, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1587-1595, 2010.
DOI : 10.1109/TIP.2010.2044959

H. Talbot and B. Appleton, Efficient complete and incomplete path openings and closings, Image and Vision Computing, vol.25, issue.4, pp.416-425, 2007.
DOI : 10.1016/j.imavis.2006.07.021

F. Cokelaer, H. Talbot, and J. Chanussot, Efficient Robust d-Dimensional Path Operators, IEEE Journal of Selected Topics in Signal Processing, vol.6, issue.7, pp.830-839, 2012.
DOI : 10.1109/JSTSP.2012.2213578

URL : https://hal.archives-ouvertes.fr/hal-00798414

O. Merveille, B. Naegel, H. Talbot, L. Najman, and N. Passat, 2D Filtering of Curvilinear Structures by Ranking the Orientation Responses of Path Operators (RORPO), Image Processing On Line, vol.7, pp.246-261, 2017.
DOI : 10.5201/ipol.2017.207

URL : https://hal.archives-ouvertes.fr/hal-01599528

A. N. Tikhonov, Regularization of incorrectly posed problems, Soviet Mathematics ? Doklady, vol.4, issue.6, pp.1624-1627, 1963.

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.1-4, 1992.
DOI : 10.1016/0167-2789(92)90242-F

C. Rhemann, C. Rother, P. Kohli, and M. Gelautz, A spatially varying PSF-based prior for alpha matting, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2149-2156, 2010.
DOI : 10.1109/CVPR.2010.5539894

URL : http://research.microsoft.com/en-us/um/people/pkohli/papers/rrkg_cvpr2010.pdf

W. Dong, L. Zhang, G. Shi, and X. Wu, Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, IEEE Transactions on Image Processing, vol.20, issue.7, pp.1838-1857, 2011.
DOI : 10.1109/TIP.2011.2108306

J. Yang, J. Wright, T. S. Huang, and Y. Ma, Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010.
DOI : 10.1109/TIP.2010.2050625

M. Fadili, J. Starck, and F. Murtagh, Inpainting and Zooming Using Sparse Representations, The Computer Journal, vol.57, issue.3, pp.64-79, 2009.
DOI : 10.1109/TIP.2005.863057

URL : https://hal.archives-ouvertes.fr/hal-00091746

T. Chan and L. Vese, Active contours without edges, IEEE Transactions on Image Processing, vol.10, issue.2, pp.266-277, 2001.
DOI : 10.1109/83.902291

URL : http://www.math.ucla.edu/~lvese/PAPERS/IEEEIP2001.pdf

T. F. 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

URL : http://www.cmla.ens-cachan.fr/fileadmin/Membres/nikolova/ChanEseNikoSiap06.pdf

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, pp.2521-2546, 2013.
DOI : 10.1137/120901751

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

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

N. Y. El-zehiry and L. Grady, Vessel segmentation using 3D elastical regularization, ISBI, Proceedings, pp.1288-1291, 2012.
DOI : 10.1109/isbi.2012.6235798

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

O. Miraucourt, A. Jezierska, H. Talbot, S. Salmon, and N. Passat, Variational method combined with Frangi vesselness for tubular object segmentation, CMBE, Proceedings, pp.485-488, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01695072

D. L. Ruderman, The statistics of natural images, Network: Computation in Neural Systems, vol.3, issue.7, pp.517-548, 1994.
DOI : 10.1103/RevModPhys.55.583

URL : https://cs.uwaterloo.ca/~mannr/cs886-w10/Ruderman-statistics.pdf

P. L. Combettes and V. R. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090

URL : https://hal.archives-ouvertes.fr/hal-00017649

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

A. Beck and M. Teboulle, Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems, IEEE Transactions on Image Processing, vol.18, issue.11, pp.2419-2434, 2009.
DOI : 10.1109/TIP.2009.2028250

URL : http://ie.technion.ac.il/%7Ebecka/papers/new-vers6.pdf

J. Staal, M. Abramoff, M. Niemeijer, M. 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

C. A. Lupas¸culupas¸cu, D. Tegolo, and E. Trucco, FABC: Retinal Vessel Segmentation Using AdaBoost, IEEE Transactions on Information Technology in Biomedicine, vol.14, issue.5, pp.1267-1274, 2010.
DOI : 10.1109/TITB.2010.2052282

M. Al-rawi, M. Qutaishat, and M. Arrar, An improved matched filter for blood vessel detection of digital retinal images, Computers in Biology and Medicine, vol.37, issue.2, pp.262-267, 2007.
DOI : 10.1016/j.compbiomed.2006.03.003

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

B. Matthews, Comparison of the predicted and observed secondary structure of T4 phage lysozyme, BBA) ? Protein Structure, pp.442-451, 1975.
DOI : 10.1016/0005-2795(75)90109-9

, ESIEE Engineering) in 2013. She obtained the PhD from Université Paris- Est in 2016. She is currently post-doctoral fellow at Université de Strasbourg. Her scientific interests include mathematical morphology, machine learning and (bio)medical imaging