International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Region-based Vessel Segmentation Using Level Set Framework

  • Yu Gang (School of Life Science and Technology, Xi'an Jiaotong University) ;
  • Lin Pan (Faculty of Software, Fujian Normal University) ;
  • Li Peng (School of Life Science and Technology, Xi'an Jiaotong University) ;
  • Bian Zhengzhong (School of Life Science and Technology, Xi'an Jiaotong University)
  • Published : 2006.10.01
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Abstract

This paper presents a novel region-based snake method for vessel segmentation. According to geometric shape analysis of the vessel structure with different scale, an efficient statistical estimation of vessel branches is introduced into the energy objective function, which applies not only the vessel intensity information, but also geometric information of line-like structure in the image. The defined energy function is minimized using the gradient descent method and a new region-based speed function is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. The narrow band algorithm in the level set framework implements the proposed method, the solution of which is steady. The segmentation experiments are shown on several images. Compared with other geometric active contour models, the proposed method is more efficient and robust.

Keywords

References

  1. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, 'Detection of blood vessels in retinal images using two dimensional matched filters,' IEEE Trans. on Medical Imaging, vol. 8, no. 3, pp. 263-269, September 1989 https://doi.org/10.1109/42.34715
  2. B. D. Thackray and A. C. Nelson, 'Semiautomatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding,' IEEE Trans. on Medical Imaging, vol. 12, no. 3, pp. 385-392, September 1993 https://doi.org/10.1109/42.241865
  3. M. Sonka, G. K. Reddy, M. D. Winniford, and S. M. Collins, 'Adaptive approach to accurate analysis of small-diameter vessels in cineangiograms,' IEEE Trans. on Medical Imaging, vol. 16, no. 1, pp. 87-95, February 1997 https://doi.org/10.1109/42.552058
  4. C. Yuan, E. Lin, J. Millard, and J. Hwang, 'Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood images,' Magnetic Resonance Imaging, vol. 17, no. 2, pp. 257-266, February 1999 https://doi.org/10.1016/S0730-725X(98)00162-3
  5. F. Zana and J. C. Klein, 'A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform,' IEEE Trans. on Medical Imaging, vol. 18, no. 5, pp. 419-428, May 1999 https://doi.org/10.1109/42.774169
  6. J. A. Sechian, Level Set Methods and Fast Marching Methods, Cambridge University Press, England, 1999
  7. R. Malladi, J. A. Sethian, and B. C. Vemuri, 'Shape modeling with front propagation: A level set approach,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158-175, February 1995 https://doi.org/10.1109/34.368173
  8. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, 'Multiscale vessel enhancement filtering,' Lecture Notes in Computer Science, vol. 1496, pp. 130-137, 1998
  9. P. Nikos, 'Geodesic active regions: A new framework to deal with frame partition problems in computer vision,' Journal of Visual Communication and Image Representation, vol. 13, pp. 249-268, 2002 https://doi.org/10.1006/jvci.2001.0475
  10. A. Yezzi, A. Tsai, and A. Willsky, 'A fully global approach to image segmentation via coupled curve evolution equations,' Journal of Visual Communication and Image Representation, vol. 13, pp. 195-216, 2002 https://doi.org/10.1006/jvci.2001.0500
  11. M. Pascal, R. Philippe, G. Francois, and G. Prederic, 'Influence of the noise model on level set active contour segmentation,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 766-803, June 2004
  12. G. Ali and C. Raphael, 'A new fast level set method,' Proc. of the 6th Signal Processing Symposium, pp. 9-11, 2004
  13. V. Caselles, R. Kimmel, and G. Spairo, 'Geodesic active contours,' International Journal of Computer Vision, vol. 22, pp. 61-79, 1997 https://doi.org/10.1023/A:1007979827043