A Level-Set Based Method for Vessel Navigation

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Abstract:

Automatic blood vessel analysis plays an important role in computer-aided diagnosis of the vessels. In the blood vessel analysis system, extraction and navigation of vessels are two crucial components, and fast extraction and automatic navigation algorithms attract more and more attention. In this paper, to accelerate the extraction of blood vessel, an initial contour algorithm is proposed to produce an initial contour for level-set method. Then, the level-set method is introduced to extract the vessel more precisely. Finally, the navigation of the extracted blood vessel is realized based on a 3D texture volume rendering algorithm based on graphics processing unit (GPU). The experimental results illustrate the effectiveness of the proposed vessel extraction and navigation scheme.

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Key Engineering Materials (Volumes 474-476)

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1345-1350

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April 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] H.K. Hahn, B. Preim, D. Selle, et al: Visualization and interaction techniques for the exploration of vascular structures, in: Proceedings of the Conference on Visualization'01, USA, San Diego(2001), pp.395-402.

DOI: 10.1109/visual.2001.964538

Google Scholar

[2] C. Kirbas, F. Quek: A review of vessel extraction techniques and algorithms, ACM Computing Surveys, Vol. 36 (2) (2004), pp.81-121.

DOI: 10.1145/1031120.1031121

Google Scholar

[3] N. Niki, Y. Kawata, H. Sato, et al: 3D imaging of blood vessels using X-ray rotational angiographic system, in: IEEE Nuclear Science Symposium and Medical Imaging Conference, USA, San Francisco(1993), Vol. 3, pp.1873-1877.

DOI: 10.1109/nssmic.1993.373618

Google Scholar

[4] C. Molina, G. Prause, P. Radeva, et al: 3-D catheter path reconstruction from biplane angiograms, in: Proceedings of the SPIE, USA, Bellingham(1998), Vol. 3338, pp.504-512.

DOI: 10.1117/12.310929

Google Scholar

[5] D. Guo, P. Richardson: Automatic vessel extraction from angiogram images, IEEE Computers in Cardiology, Vol. 25 (14) (1998), pp.441-444.

DOI: 10.1109/cic.1998.731897

Google Scholar

[6] B. Preim, S. Oeltze: 3D visualization of vasculature: an overview, Visualization in Medicine and Life Sciences, Springer-Verlag, Berlin(2007).

DOI: 10.1007/978-3-540-72630-2_3

Google Scholar

[7] Y. Sato, N. Shiraga, S. Nakajima, et al: Local maximum intensity projection (LMIP): a new rendering method for vascular visualization, Journal of Computer Aided Tomography, Vol. 22 (6) (1998), pp.912-917.

DOI: 10.1097/00004728-199811000-00014

Google Scholar

[8] A. Hoover, V. Kouznetsova, M. Goldbaum: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging, Vol. 19 (3) (2000), pp.203-210.

DOI: 10.1109/42.845178

Google Scholar

[9] S. C. Chaudhuri, N. Katz, M. Nelson, et al: Detection of blood vessels in retinal images using two dimensional blood vessel filters, IEEE Transactions on Medical Imaging, Vol. 8 (3) (1989), pp.263-269.

DOI: 10.1109/42.34715

Google Scholar

[10] C. Y. Xu, J. L. Prince: Snakes, shapes, and gradient vector flow, IEEE Transactions on Image Process, Vol. 7 (3) (1998), pp.359-369.

DOI: 10.1109/83.661186

Google Scholar

[11] K. W. Sum, P. Y. S. Cheung: Boundary vector field for parametric active contours, Pattern Recognition, Vol. 40 (6) (2007), pp.1635-1645.

DOI: 10.1016/j.patcog.2006.11.006

Google Scholar

[12] H. Luo, Q. Lu, R. S. Acharya, et al: Robust snake model, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island(2000), Vol. 1, pp.452-457.

DOI: 10.1109/cvpr.2000.855854

Google Scholar

[13] Y. Tolias, S. M. Panas: A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering, IEEE Transactions on Medical Imaging, Vol. 17 (2) (1998), pp.263-273.

DOI: 10.1109/42.700738

Google Scholar

[14] S. Osher, J.A. Sethian: Fronts propagating with curvature dependent speed: algorithms based on the Hamilton-Jacobi formulation, Journal of Computational Physics, Vol. 79(1)(1988), pp.12-49.

DOI: 10.1016/0021-9991(88)90002-2

Google Scholar

[15] L. Alvarez, P.L. Lions, J.M. Morel: Image selective smoothing and edge detection by nonlinear diffusion, SIAM Journal on Numerical Analysis, Vol. 29(3)(1992), pp.845-866.

DOI: 10.1137/0729052

Google Scholar

[16] R. Malladi, J.A. Sethian, B. Vemuri: Shape modeling with front propagation: a level set approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17(2)(1995), pp.158-174.

DOI: 10.1109/34.368173

Google Scholar

[17] C. Samon, L. Blanc-Feraud, G. Aubert, et al: Level set model for image classification, International Journal of Computer Vision, Vol. 40(3)(2000), pp.187-197.

Google Scholar

[18] B. Preim, S. Oeltze: 3D visualization of vasculature: an overview, Visualization in Medicine and Life Sciences, Springer-Verlag, Berlin(2007).

DOI: 10.1007/978-3-540-72630-2_3

Google Scholar

[19] Q.X. Gao, J.Z. Yang, D.Z. Zhao, et al: Pulmonary vessel for X-ray images segmented through canny level-et, Journal of System Simulation, Vol. 20(20)(2008), pp.5534-5537.

Google Scholar

[20] J.A. Sethian: Adaptive fast marching and level set methods for propagating interfaces, Acta Math Univ. Comenianae, Vol. LXVII(1)(1998), pp.3-15.

Google Scholar

[21] A. Yezzi, S. Kichenassamy, A. Kumar, et al: A geometric snake model for segmentation of medical imagery, IEEE Transactions on Medical Imaging, Vol. 16(2)(1997), pp.199-209.

DOI: 10.1109/42.563665

Google Scholar

[22] X.R. Lv, X.B. Gao, H. Zou: Interactive curved planar reformation based on snake model, Computed Medical Imaging and Graphics, Vol. 32(8)(2008), pp.662-669.

DOI: 10.1016/j.compmedimag.2008.08.002

Google Scholar

[23] J.G. Sun, C.G. Yang: Computer Graphics (Tsinghua University Press, Beijing 1995).

Google Scholar