Abstract
Tracking of tubular elongated structures is an important goal in a wide range of biomedical imaging applications. A Bayesian tube tracking algorithm is presented that allows to easily incorporate a priori knowledge. Because probabilistic tube tracking algorithms are computationally complex, steps towards a computational efficient implementation are suggested in this paper.
The algorithm is evaluated on 2D and 3D synthetic data with different noise levels and clinical CTA data. The approach shows good performance on data with high levels of Gaussian noise.
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Caussin, C., Larchez, C., Ghostine, S., Pesenti-Rossi, D., Daoud, B., Habis, M., Sigal-Cinqualbre, A., Perrier, E., Angel, C.-Y., Lancelin, B., Paul, J.-F.: Comparison of coronary minimal lumen area quantification by sixty-four-slice computed tomography versus intravascular ultrasound for intermediate stenosis. American Journal of Cardiology 98(7), 871–876 (2006)
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Computing Surveys 36(2), 81–121 (2004)
Suri, J.S., Liu, K., Reden, L., Laxminarayan, S.: A review on MR vascular image processing algorithms: acquisition and prefiltering: part I. IEEE Trans Inf. Technol Biomed 6(4), 324–337 (2002)
Florin, C., Paragios, N., Williams, J.: Globally optimal active contours, sequential monte carlo and on-line learning for vessel segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 476–489. Springer, Heidelberg (2006)
Aylward, S., Bullit, E.: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Transactions on Medical Imaging 21(2), 61–75 (2002)
Lorigo, L.M., Faugeras, O.D., Grimson, W.E.L., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.-F.: Curves: Curve evolution for vessel segmentation. Medical Image Analysis 5, 195–206 (2001)
Myatt, D., Nasuto, S., Maybank, S.: Towards the automatic reconstruction of dendritic trees using particle filters. In: Linear Statistical Signal Processing Workshop (on CD-ROM) (2006)
Doucet, A., Godsill, S., Andrieu, C.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)
Weisstein, E.W.: Rotation Matrix. From MathWorld–A Wolfram Web Resource
Saff, E., Kuijlaars, A.: Distributing many points on a sphere. The Mathematical Intelligence 19(1), 5–11 (1997)
Rose, A.: Vision: Human and Electronic. Plenum Press, New York (1973)
Manniesing, R., Niessen, W.: Multiscale vessel enhancing diffusion in CT angiography noise filtering. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 138–149. Springer, Heidelberg (2005)
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Schaap, M., Smal, I., Metz, C., van Walsum, T., Niessen, W. (2007). Bayesian Tracking of Elongated Structures in 3D Images. In: Karssemeijer, N., Lelieveldt, B. (eds) Information Processing in Medical Imaging. IPMI 2007. Lecture Notes in Computer Science, vol 4584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73273-0_7
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DOI: https://doi.org/10.1007/978-3-540-73273-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73272-3
Online ISBN: 978-3-540-73273-0
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