Abstract
We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and particle filter tracking. In comparison to earlier work we utilize a 3D intensity model as the measurement model of the particle filter, thus a more realistic 3D appearance model is used that directly represents the image intensities of 3D tubular structures within semi-global regions-of-interest. We have successfully applied our approach using 3D synthetic images and real 3D MRA image data of the human pelvis.
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References
Frangi AF, Niessen WJ, Vincken KL, et al. Multiscale vessel enhancement filtering. Proc MICCAI. 1998; p. 130–137.
Wink O, Niessen WJ, Viergever MA. Multiscale vessel tracking. IEEE Trans Med Imaging. 2004;23(l):130–133.
Volkau I, Ng TT, Marchenko Y, et al. On geometric modeling of the human intracranial venous system. IEEE Trans Med Imaging. 2008;27(6):745–51.
Noordmans HJ, Smeulders AWM. High accuracy tracking of 2D/3D curved line structures by consecutive cross-section matching. Pattern Recogn Lett. 1998;19(1):97–111.
Wörz S, Rohr K. Segmentation and quantification of human vessels using a 3-D cylindrical intensity model. IEEE Trans Image Process. 2007;16(8): 1994–2004.
Guerrero J, Salcudean SE, McEwen JA, et al. Real-time vessel segmentation and tracking for ultrasound imaging applications. IEEE Trans Medical Imaging. 2007;26(8):1079–1090.
Florin C, Paragios N, Williams J. Globally optimal active contours, sequential Monte Carlo and on-line learning for vessel segmentation. Proc ECCV. 2006; p. 476–489.
Schaap M, Manniesing R, Smal I, et al. Bayesian tracking of tubular structures and its application to carotid arteries in CTA. Proc MICCAI. 2007; p. 562–570.
Lesage D, Angelini ED, Bloch I, et al. Medial-based bayesian tracking for vascular segmentation: Application to coronary arteries in 3D CT angiography. Proc IEEE ISBI. 2008; p. 268–271.
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© 2009 Springer-Verlag Berlin Heidelberg
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Wörz, S., Godinez, W.J., Rohr, K. (2009). Probabilistic Tracking and Model-Based Segmentation of 3D Tubular Structures. In: Meinzer, HP., Deserno, T.M., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2009. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93860-6_9
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DOI: https://doi.org/10.1007/978-3-540-93860-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93859-0
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