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Erschienen in:
Object Segmentation and Markov Random Fields Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Monte Carlo Sampling for the Segmentation of Tubular Structures

verfasst von : C. Florin, N. Paragios, J. Williams

Erschienen in: Handbook of Biomedical Imaging

Verlag: Springer US

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Abstract

In this paper, we present a multiple hypotheses testing for the segmentation of tubular structures in medical imaging that addresses appearance (scanner artifacts, pathologies,…) and geometric (bifurcations) non-linearities. Our method represents vessels/tubular structures as sequences of state vectors (vessel cuts/cross-sections), which are described by the position of the corresponding plane, the center of the vessel in this plane and its radius. Thus, 3D segmentation consists in finding the optimal sequence of 2D planes normal to the vessel’s centerline. This sequence of planes is modeled by a probability density function (pdf for short) which is maximized with respect to the parameters of the state vector. Such a pdf is approximated in a non-parametric way, the Particle Filter approach, that is able to express multiple hypotheses (branches). Validation using ground truth from clinical experts and very promising experimental results for the segmentation of the coronaries demonstrates the potential of the proposed approach.

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Vorheriges Kapitel Integration of Topological Constraints in Medical Image Segmentation
Nächstes Kapitel Non-rigid registration using free-form deformations
Literatur
1.
S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Process., 50:174–188, 2002.CrossRef
2.
B. Avants and J. Williams. An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images. In Med. Image Comput. Comput. Assist. Interv. Int. Conf., volume 3749, pages 707–716. Springer, 2000.
3.
S. Bouix, K. Siddiqi, and A. R. Tannenbaum. Flux driven automatic centerline extraction. In Med. Image Anal., volume 9, pages 209–221(3), 2005.CrossRef
4.
V. Caselles, F. Catté, B. Coll, and F. Dibos. A geometric model for active contours in image processing. Numerische Mathematik, 66(1):1–31, 1993.CrossRefMATHMathSciNet
5.
T. Deschamps. Curve and Shape Extraction with Minimal Path and Level-Sets techniques-Applications to 3D Medical Imaging. PhD thesis, Université Paris-IX Dauphine, Place du maréchal de Lattre de Tassigny, 75775 Paris Cedex, Dec. 2001.
6.
T. Deschamps and L. Cohen. Fast extraction of tubular and tree 3d surfaces with front propagation methods. In IARP International Conference on Pattern Recognition, volume 1, pages 731–734. IEEE Computer Society, 2002.
7.
T. Deschamps and L. D. Cohen. Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Med. Image Anal., 5(4):281–299, Dec. 2001.CrossRef
8.
A. Doucet, J. de Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, 2001.
9.
A. Doucet, N. Gordon, and C. Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 10(3):197–208, 2000.CrossRef
10.
R. Duda and P. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.MATH
11.
M. Figueiredo and J. Leitao. A nonsmoothing approach to the estimation of vessel contours in angiograms. IEEE Trans. Med. Imaging, 14:162–172, 1995.CrossRef
12.
C. Florin, N. Paragios, and J. Williams. Particle filters, a Quasi-Monte Carlo solution for segmentation of coronaries. In Med. Image Comput. Comput. Assist. Interv. Int. Conf., pages 246–253, 2005.
13.
C. Florin, N. Paragios, and J. Williams. Globally optimal active contours, sequential monte carlo and on-line learning for vessel segmentation. In European Conference on Computer Vision, volume 3953, pages 476–489, 2006.
14.
A. Frangi, W. Niessen, P. Nederkoorn, O. Elgersma, and M. Viergever. Three-dimensional model-based stenosis quantification of the carotid arteries from contrast-enhanced MR angiography. In IEEE Mathematical Methods in Biomedical Image Analysis, pages 110–118, 2000.
15.
A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever. Multiscale vessel enhancement filtering. Lecture Notes in Computer Science, 1496, 1998.
16.
N. Gordon. Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. IEEE Proceedings, 140:107–113, 1993.
17.
M. Hart and L. Holley. A method of Automated Coronary Artery Tracking in Unsubtracted Angiograms. IEEE Comput. in Cardiol., pages 93–96, 1993.
18.
M. Isard and A. Blake. Contour Tracking by Stochastic Propagation of Conditional Density. In European Conference on Computer Vision, volume I, pages 343–356,1996.
19.
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, 80:130–171, 2000.CrossRefMATH
20.
L. Lorigo, O. Faugeras, E. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C. Westin. Codimension-Two Geodesic Active Controus for the Segmentation of Tubular Structures. In IEEE Conference on Computer Vision and Pattern Recognition, pages I:444–451, 2000.
21.
R. Malladi and J. Sethian. A Real-Time Algorithm for Medical Shape Recovery. In IEEE International Conference in Computer Vision, pages 304–310, 1998.
22.
D. Nain, A. Yezzi, and G. Turk. Vessel Segmentation Using a Shape Driven Flow. In Med. Image Comput. Comput. Assist. Interv. Int. Conf., 1, pages 51–59. Springer, 2004.
23.
T. O´ Donnell, T. Boult, X. Fang, and A. Gupta. The Extruded Generalized Cylider: A Deformable Model for Object Recovery. In IEEE Conference on Computer Vision and Pattern Recognition, pages 174–181, 1994.
24.
S. Osher and N. Paragios. Geometric Level Set Methods in Imaging, Vision and Graphics. Springer Verlag, 2003.MATH
25.
R. Petrocelli, K. Manbeck, and J. Elion. Three Dimensional Structure Recognition in Digital Angiograms using Gauss-Markov Models. In Comput. in Radiol., pages 101–104. IEEE, 1993.
26.
F. L. Ruberg. Computed Tomography of the Coronary Arteries. London, UK: Taylor & Francis, 2005.CrossRef
27.
D. Rueckert, P. Burger, S. Forbat, R. Mohiadin, and G. Yang. Automatic Tracking of the Aorta in Cardiovascular MR images using Deformable Models. IEEE Trans. Med. Imaging, 16: 581–590, 1997.CrossRef
28.
J. Sethian. A Review of the Theory, Algorithms, and Applications of Level Set Methods for Propagating Interfaces. Cambridge University Press, pages 487–499, 1995.
29.
J. Sethian. Level Set Methods. Cambridge University Press, 1996.MATH
30.
M. Sofka and C. V. Stewart. Retinal vessel extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imaging, 25(12):1531–1546, 2006.CrossRef
31.
E. Sorantin, C. Halmai, B. Erbohelyi, K. Palagyi, K. Nyul, K. Olle, B. Geiger, F. Lindbichler, G. Friedrich, and K. Kiesler. Spiral-CT-based assessment of Tracheal Stenoses using 3D Skeletonization. IEEE Trans. Med. Imaging, 21:263–273, 2002.CrossRef
32.
K. Toyama and A. Blake. Probabilistic Tracking in a Metric Space. In IEEE International Conference in Computer Vision, pages 50–59, 2001.
33.
J. Tsitsiklis. Efficient Algorithms for Globally Optimal Trajectories. IEEE Transactions on Automatic Control, 40:1528–1538, 1995.CrossRefMATHMathSciNet
34.
W. West. Modeling with mixtures. In J. Bernardo, J. Berger, A. Dawid, and A. Smith, editors, Bayesian Statistics 4. Clarendon Press, 1993.
35.
O. Wink, W. J. Niessen, and M. A. Viergever. Multiscale vessel tracking. IEEE Trans. Med. Imaging, 23(1):130–133, 2004.CrossRef
36.
S. Y., N. S., S. N., A. H., Y. S., K. T., G. G., and K. R. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal., 2:143–168(26), 1998.
37.
P. Yim, P. Choyke, and R. Summers. Grayscale Skeletonization of Small Vessels in Magnetic Resonance Angiography. IEEE Trans. Med. Imaging, 19:568–576, 2000.CrossRef
Metadaten
Titel
Monte Carlo Sampling for the Segmentation of Tubular Structures
verfasst von
C. Florin
N. Paragios
J. Williams
Copyright-Jahr
2015
Verlag
Springer US
Buch
Handbook of Biomedical Imaging

Print ISBN: 978-0-387-09748-0

Electronic ISBN: 978-0-387-09749-7

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-0-387-09749-7_14

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