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Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement

Authors:

Fethallah Benmansour,

Laurent D. CohenAuthors Info & Claims

International Journal of Computer Vision, Volume 92, Issue 2

Pages 192 - 210

https://doi.org/10.1007/s11263-010-0331-0

Published: 01 April 2011 Publication History

Abstract

We present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are minimal paths solved using the fast marching algorithm. This allows interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric. The designed metric is well oriented along the direction of the vessel, admits higher velocity on the centerline, and provides a good estimate of the vessel radius. Based on the optimally oriented flux this measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. We obtain promising results on noisy synthetic and real 2D and 3D images and we present a clinical validation.

References

[1]

Benmansour, F. (2009). Minimal path method applied to medical imaging: tubular structure and surface segmentation using multiscaled anisotropy and recursive keypoints detection. Ph.D. Thesis, Universit&#233; Paris Dauphine.

[2]

Benmansour, F., &amp; Cohen, L.D. (2009). Tubular anisotropy segmentation. In SSVM (pp. 14-25).

[3]

Bornemann, F., &amp; Rasch, C. (2006). Finite-element discretization of static Hamilton-Jacobi equations based on a local variational principle. Computing and Visualization in Science, 9(2).

[4]

Caselles, V., Kimmel, R., &amp; Sapiro, G. (1995). Geodesic active contours. In IEEE international conference in computer vision (ICCV'95) (pp. 694-699).

[5]

Caselles, V., Kimmel, R., &amp; Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22, 61-79.

Digital Library

[6]

Chan, T. F., &amp; Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266-277.

Digital Library

[7]

Chern, S.-S. (1996). Finsler geometry is just Riemannian geometry without the quadratic restriction. Notices of the American Mathematical Society, 43, 959-963.

[8]

Chopp, D. L. (2001). Replacing iterative algorithms with single-pass algorithms. Proceedings of the National Academy of Science of the USA, 98(20), 10992-10993.

[9]

Cohen, L. D., &amp; Deschamps, T. (2007). Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging. Computer Methods in Biomechanics and Biomedical Engineering, 10(4), 289-305.

[10]

Cohen, L. D., &amp; Kimmel, R. (1997). Global minimum for active contour models: a minimal path approach. International Journal of Computer Vision, 24, 57-78.

Digital Library

[11]

Davatzikos, C. A., &amp; Prince, J. L. (1995). An active contour model for mapping the cortex. IEEE Transactions on Medical Imaging, 14(1), 65-80.

[12]

Deschamps, T., &amp; Cohen, L. D. (2001). Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Medical Image Analysis, 5, 281-299.

[13]

Deschamps, T., &amp; Cohen, L. D. (2002). Fast extraction of tubular and tree 3D surfaces with front propagation methods. In IEEE international conference on pattern recognition (ICPR'02) (pp. 731- 734).

[14]

Descoteaux, M., Collins, L., &amp; Siddiqi, K. (2008). A geometric flow for segmenting vasculature in proton-density weighted MRI. Medical Image Analysis, 12(4), 497-513.

[15]

Dijkstra, E. W. (1959). A note on two problems in connection with graphs. Numerische Mathematic, 1, 269-271.

Digital Library

[16]

Evans, C. L. (1998). Partial differential equations. Providence: American Mathematical Society.

[17]

Frangi, A. F., Niessen, W. J., Vincken, K. L., &amp; Viergever, M. A. (1998). Multiscale vessel enhancement filtering. In Lecture notes in computer science (Vol. 1496, pp. 130-137). Berlin: Springer.

[18]

Freeman, W. T., &amp; Adelson, E. H. (1991). The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9), 891-906.

Digital Library

[19]

Gooya, A., Liao, H., Matsumiya, K., Masamune, K., Masutani, Y., &amp; Dohi, T. (2008a). A variational method for geometric regularization of vascular segmentation in medical images. IEEE Transactions on Image Processing, 17(8), 1295-1312.

[20]

Gooya, A., Dohi, T., Sakuma, I., &amp; Liao, H. (2008b). Anisotropic Haralick edge detection scheme with application to vessel segmentation. In MIAR'08: Proceedings of the 4th international workshop on medical imaging and augmented reality (pp. 430-438). Berlin: Springer.

[21]

Gooya, A., Dohi, T., Sakuma, I., &amp; Liao, H. (2008c). R-PLUS: a Riemannian anisotropic edge detection scheme for vascular segmentation. In MICCAI'08: Proceedings of the 11th international conference on medical image computing and computer-assisted intervention--Part I (pp. 262-269). Berlin: Springer.

[22]

Hameeteman, R., Freiman, M., Zuluaga, M. A., Joskowicz, L., Rozie, S., van Gils, M. J., van den Borne, L., Sosna, J., Berman, P., Cohen, N., Douek, P., S&#225;nchez, I., Aissat, M., van der Lugt, A., Krestin, G. P., Niessen, W. J., &amp; van Walsum, T. (2009). Carotid lumen segmentation and stenosis grading challenge. In Workshop in international conference on medical image computing and computer assisted intervention, September 2009.

[23]

Hern&#225;ndez Hoyos, M., Serfaty, J. M., Maghiar, A., Mansard, C., Orkisz, M., Magnin, I. E., &amp; Douek, P. (2006). Evaluation of semi-automatic arterial stenosis quantification. International Journal of Computer Assisted Radiology, 1(3), 167-175.

[24]

Holtzman-Gazit, M., Kimmel, R., Peled, N., &amp; Goldsher, D. (2006). Segmentation of thin structures in volumetric medical images. IEEE Transactions on Image Processing, 15, 354-363.

Digital Library

[25]

Tavares, J., &amp; Jorge, R. (2009). In Geodesic methods for shape and surface processing: Vol. 13. Advances in computational vision and medical image processing: methods and applications (pp. 29-56). Berlin: Springer.

[26]

Jacob, M., &amp; Unser, M. (2004). Design of steerable filters for feature detection using Canny-like criteria. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 1007-1019.

Digital Library

[27]

Jbabdi, S., Bellec, P., Toro, R., Daunizeau, J., P&#233;l&#233;grini-Issac, M., &amp; Benali, H. (2008). Accurate anisotropic fast marching for diffusion-based geodesic tractography. International Journal of Biomedical Imaging, 2008(1), 1-12.

Digital Library

[28]

Kimmel, R., &amp; Bruckstein, A. (2003). Regularized Laplacian zero crossings as optimal edge integrators. International Journal of Computer Vision, 53, 225-243.

Digital Library

[29]

Kirbas, C., &amp; Quek, F. K. H. (2004). A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 36, 81-121.

Digital Library

[30]

Konukoglu, E., Sermesant, M., Clatz, O., Peyrat, J.-M., Delingette, H., &amp; Ayache, N. (2007). A recursive anisotropic fast marching approach to reaction diffusion equation: application to tumor growth modeling. In Lecture notes in computer science: Vol. 4584. Proceedings of the 20th international conference on information processing in medical imaging (IPMI'07) (pp. 686-699). Berlin: Springer.

[31]

Krissian, K. (2002). Flux-based anisotropic diffusion applied to enhancement of 3-D angiogram. IEEE Transactions on Medical Imaging, 21(11), 1440-1442.

[32]

Krissian, K., Malandain, G., &amp; Ayache, N. (1997). Directional anisotropic diffusion applied to segmentation of vessels in 3D images. In SCALE-SPACE'97: Proceedings of the first international conference on scale-space theory in computer vision, London, UK (pp. 345-348). Berlin: Springer.

[33]

Law, W. K., &amp; Chung, A. C. S. (2006). Segmentation of vessels using weighted local variances and an active contour model. In CVPRW '06: Proceedings of the 2006 conference on computer vision and pattern recognition workshop, Washington, DC, USA (p. 83). Los Alamitos: IEEE Computer Society.

Digital Library

[34]

Law, M. W. K., &amp; Chung, A. C. S. (2007). Weighted local variance-based edge detection and its application to vascular segmentation in magnetic resonance angiography. IEEE Transactions on Medical Imaging, 26(9), 1224-1241.

[35]

Law, M. W., &amp; Chung, A. C. (2008). Three dimensional curvilinear structure detection using optimally oriented flux. In ECCV'08: Proceedings of the 10th European con computer vision, Berlin, Heidelberg (pp. 368-382). Berlin: Springer.

[36]

Lenglet, C., Prados, E., Pons, J.-P., Deriche, R. &amp; Faugeras, O. (2009). Brain connectivity mapping using Riemannian geometry, control theory and PDEs. SIAM Journal on Imaging Sciences (SIIMS), 2(2), 285-322.

Digital Library

[37]

Lesage, D., Angelini, E. D., Bloch, I., &amp; Funka-Lea, G. (2009a). Bayesian maximal paths for coronary artery segmentation from 3d ct angiograms. In Yang, G.-Z., Hawkes, D. J., Rueckert, D., Noble, J. A., &amp; Taylor, C. J. (Eds.), Lecture notes in computer science: Vol. 5761. International conference on medical image computing and computer assisted intervention (1) (pp. 222-229). Berlin: Springer.

[38]

Lesage, D., Angelini, E. D., Bloch, I., &amp; Funka-Lea, G. (2009b). A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Medical Image Analysis, 13(6), 819-845.

[39]

Li, H., &amp; Yezzi, A. (2006). Vessels as 4D curves: global minimal 4D paths to extract 3D tubular surfaces. In IEEE conference on computer vision and pattern recognition (CVPR'06), Workshop MM- BIA06 (p. 82).

[40]

Li, H., &amp; Yezzi, A. (2007). Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Transactions on Medical Imaging, 26(9), 1213-1223.

[41]

Lin, Q. (2003). Enhancement, extraction, and visualization of 3D volume data. Ph.D. Thesis, Linkopings Universitet.

[42]

Lindeberg, T. (1998). Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision, 30, 465-470.

[43]

Lions, P. L. (1982). Generalized solutions of Hamilton-Jacobi equations. Research notes in mathematics (Vol. 69). London: Pitman.

[44]

Lorenz, C., Carlsen, I.-C., Buzug, T. M., Fassnacht, C., &amp; Weese, J. (1997). Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In CVRMed-MRCAS'97: Proceedings of the first joint conference on computer vision, virtual reality and robotics in medicine and medial robotics and computer-assisted surgery, London, UK (pp. 233-242). Berlin: Springer.

[45]

Manniesing, R., Viergever, M. A., &amp; Niessen, W. J. (2006). Vessel enhancing diffusion: a scale space representation of vessel structures. Medical Image Analysis, 10(6), 815-825.

[46]

Manniesing, R., Viergever, M. A., &amp; Niessen, W. J. (2007). Vessel axis tracking using topology constrained surface evolution. IEEE Transactions on Medical Imaging, 26(3), 309-316.

[47]

Melonakos, J., Pichon, E., Angenent, S., &amp; Tannenbaum, A. (2008). Finsler active contours. IEEE Transactions Pattern Analysis and Machine Intelligence, 30(3), 412-423.

Digital Library

[48]

Mille, J., Benmansour, F., &amp; Cohen, L. D. (2009). Carotid lumen segmentation based on tubular anisotropy and contours without edges. Insight Journal. http://www.insight-journal.org/browse/ publication/670.

[49]

Mohan, V., Sundaramoorthi, G., Melonakos, J., Niethammer, M., Kubicki, M., &amp; Tannenbaum, A. (2008). Tubular surface evolution for segmentation of the cingulum bundle from DW-MRI. In Mathematical methods in computational anatomy.

[50]

Nain, D., Yezzi, A., &amp; Turk, G. (2004). Vessel segmentation using a shape driven flow. In Medical imaging computing and computer-assisted intervention (MICCAI'04) (pp. 51-59).

[51]

Nemitz, O., Rumpf, M., Tasdizen, T., &amp; Whitaker, R. (2007). Anisotropic curvature motion for structure enhancing smoothing of 3D MR angiography data. Journal of Mathematical Imaging and Vision, 27(3), 217-229.

Digital Library

[52]

Orkisz, M., Fl&#243;rez Valencia, L., &amp; Hern&#225;ndez Hoyos, M. (2008). Models, algorithms and applications in vascular image segmentation. Machine Graphics and Vision, 17(1), 5-33.

Digital Library

[53]

Rouy, E., &amp; Tourin, A. (1992). A viscosity solution approach to shape from shading. SIAM Journal on Numerical Analysis, 29, 867- 884.

Digital Library

[54]

Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., &amp; Kikinis, R. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis, 2(2), 143-168.

[55]

Sethian, J. A. (1996). A fast marching level set for monotonically advancing fronts. Proceedings of the National Academy of Sciences, 93, 1591-1595.

[56]

Sethian, J. A., &amp; Vladimirsky, A. (2000). Fast methods for the Eikonal and related Hamilton-Jacobi equations on unstructured meshes. Proceedings of the National Academy of Sciences, 97(11), 5699- 5703.

[57]

Siddiqi, K., &amp; Vasilevskiy, A. (2001). 3d flux maximizing flows. In EMMCVPR'01: Proceedings of the third international workshop on energy minimization methods in computer vision and pattern recognition, London, UK (pp. 636-650). Berlin: Springer.

[58]

Sundaramoorthi, G., Yezzi, A., Mennucci, A. C., &amp; Sapiro, G. (2009). New possibilities with Sobolev active contours. International Journal of Computer Vision, 84(2), 113-129.

Digital Library

[59]

Tsitsiklis, J. N. (1995). Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control, 40, 1528-1538.

[60]

Weber, O., Devir, Y. S., Bronstein, A. M., Bronstein, M. M., &amp; Kimmel, R. (2008). Parallel algorithms for approximation of distance maps on parametric surfaces. ACM Transactions on Graphics, 27(4). http://portal.acm.org/citation.cfm?id=1409625.1409626.

[61]

Weickert, J. (1999). Coherence-enhancing diffusion filtering. International Journal of Computer Vision, 31(2-3), 111-127.

Digital Library

Cited By

van den Berg NSmets BPai GMirebeau JDuits R(2024)Geodesic Tracking via New Data-Driven Connections of Cartan Type for Vascular Tree TrackingJournal of Mathematical Imaging and Vision10.1007/s10851-023-01170-x66:2(198-230)Online publication date: 1-Apr-2024
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Wang DZhang ZZhao ZLiu YChen YWang L(2022)PointScatter: Point Set Representation for Tubular Structure ExtractionComputer Vision – ECCV 202210.1007/978-3-031-19803-8_22(366-383)Online publication date: 23-Oct-2022
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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 92, Issue 2

April 2011

99 pages

ISSN:0920-5691

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Copyright © Copyright © 2011 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

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Published: 01 April 2011

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Cited By

van den Berg NSmets BPai GMirebeau JDuits R(2024)Geodesic Tracking via New Data-Driven Connections of Cartan Type for Vascular Tree TrackingJournal of Mathematical Imaging and Vision10.1007/s10851-023-01170-x66:2(198-230)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10851-023-01170-x
Tangwiriysakul CBorges PMoriconi SWright PMah YTeo JNachev POurselin SCardoso M(2023)Framework to Generate Perfusion Map from CT and CTA Images in Patients with Acute Ischemic Stroke: A Longitudinal and Cross-Sectional StudyBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries10.1007/978-3-031-76160-7_15(154-162)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-76160-7_15
Wang DZhang ZZhao ZLiu YChen YWang L(2022)PointScatter: Point Set Representation for Tubular Structure ExtractionComputer Vision – ECCV 202210.1007/978-3-031-19803-8_22(366-383)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19803-8_22
Zhao FChen YHou YHe X(2019)Segmentation of blood vessels using rule-based and machine-learning-based methodsMultimedia Systems10.1007/s00530-017-0580-725:2(109-118)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s00530-017-0580-7
Chen DZhang JCohen L(2018)Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive AnisotropyIEEE Transactions on Image Processing10.1109/TIP.2018.287428228:3(1271-1284)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1109/TIP.2018.2874282
Méndez-Rial RMartín-Herrero J(2018)Separable Anisotropic DiffusionInternational Journal of Computer Vision10.1007/s11263-017-1060-4126:6(651-670)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11263-017-1060-4
Mirebeau J(2018)Fast-Marching Methods for Curvature Penalized Shortest PathsJournal of Mathematical Imaging and Vision10.1007/s10851-017-0778-560:6(784-815)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10851-017-0778-5
Chen DCohen L(2018)Fast Asymmetric Fronts Propagation for Image SegmentationJournal of Mathematical Imaging and Vision10.1007/s10851-017-0776-760:6(766-783)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10851-017-0776-7
Chen DMirebeau JCohen L(2017)Global Minimum for a Finsler Elastica Minimal Path ApproachInternational Journal of Computer Vision10.1007/s11263-016-0975-5122:3(458-483)Online publication date: 1-May-2017
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Liao WRohr KKang CCho ZWorz S(2016)Automatic 3D Segmentation and Quantification of Lenticulostriate Arteries from High-Resolution 7 Tesla MRA ImagesIEEE Transactions on Image Processing10.1109/TIP.2015.249908525:1(400-413)Online publication date: 1-Jan-2016
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