Abstract
In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Avants, B.B., Williams, J.P.: An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 707–716. Springer, Heidelberg (2000)
Descoteaux, M., Collins, L., Siddiqi, K.: Geometric flows for segmenting vasculature in MRI: Theory and validation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 500–507. Springer, Heidelberg (2004)
Doucet, A., de Freitas, J., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Fearnhead, P., Clifford, P.: Online inference for well-log data. Journal of the Royal Statistical Society 65, 887–899 (2003)
Figueiredo, M., Leitao, J.: A nonsmoothing approach to the estimation of of vessel controus in angiograms. IEEE Transactions on Medical Imaging 14, 162–172 (1995)
Frangi, A., Niessen, W., Nederkoorn, P., Elgersma, O., Viergever, M.: Three-dimensional model-based stenosis quantification of the carotid arteries from contrast-enhanced MR angiography. In: Mathematical Methods in Biomedical Image Analysis, pp. 110–118 (2000)
Gordon, N.: Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. IEE Proceedings 140, 107–113 (1993)
Gordon, N.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)
Gordon, N.: A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50, 174–188 (2002)
Hart, M., Holley, L.: A method of Automated Coronary Artery Trackin in Unsubtracted Angiograms. IEEE Computers in Cardiology, 93–96 (1993)
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based detection of tubular structures in 3d images. Computer Vision and Image Understanding 80, 130–171 (2000)
Malladi, R., Sethian, J.: A Real-Time Algorithm for Medical Shape Recovery. In: International Conference on Computer Vision, pp. 304–310 (1998)
Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 22–26 (1985)
Nain, D., Yezzi, A., Turk, G.: Vessel Segmentation Using a Shape Driven Flow. In: Medical Imaging Copmuting and Computer-Assisted Intervention (2004)
O’Donnell, T., Boult, T., Fang, X., Gupta, A.: The Extruded Generalized Cylider: A Deformable Model for Object Recovery. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 174–181 (1994)
Osher, S., Paragios, N.: Geometric Level Set Methods in Imaging, Vision and Graphics. Springer, Heidelberg (2003)
Petrocelli, R., Manbeck, K., Elion, J.: Three Dimentional Structue Recognition in Digital Angiograms using Gauss-Markov Models. IEEE Computers in Radiology, 101–104 (1993)
Rueckert, D., Burger, P., Forbat, S., Mohiadin, R., Yang, G.: Automatic Tracking of the Aorta in Cardiovascular MR images using Deformable Models. IEEE Transactions on Medical Imaging 16, 581–590 (1997)
Sorantin, E., Halmai, C., Erbohelyi, B., Palagyi, K., Nyul, K., Olle, K., Geiger, B., Lindbichler, F., Friedrich, G., Kiesler, K.: Spiral-CT-based assesment of Tracheal Stenoses using 3D Skeletonization. IEEE Transactions on Medical Imaging 21, 263–273 (2002)
West, W.: Modelling with mixtures. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds.) Bayesian Statistics. Clarendon Press, Oxford (1993)
Yim, P., Choyke, P., Summers, R.: Grayscale Skeletonization of Small Vessels in Magnetic Resonance Angiography. IEEE Transactions on Medical Imaging 19, 568–576 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Florin, C., Paragios, N., Williams, J. (2005). Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_31
Download citation
DOI: https://doi.org/10.1007/11566465_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)