Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

A Fast Lower Extremity Vessel Segmentation Method for Large CT Data Sets Using 3-Dimensional Seeded Region Growing and Branch Classification

  • Kim, Dong-Sung (School of Electronic Engineering, Soongsil University)
  • Published : 2008.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Segmenting vessels in lower extremity CT images is very difficult because of gray level variation, connection to bones, and their small sizes. Instead of segmenting vessels, we propose an approach that segments bones and subtracts them from the original CT images. The subtracted images can contain not only connected vessel structures but also isolated vessels, which are very difficult to detect using conventional vessel segmentation methods. The proposed method initially grows a 3-dimensional (3D) volume with a seeded region growing (SRG) using an adaptive threshold and then detects junctions and forked branches. The forked branches are classified into either bone branches or vessel branches based on appearance, shape, size change, and moving velocity of the branch. The final volume is re-grown by collecting connected bone branches. The algorithm has produced promising results for segmenting bone structures in several tens of vessel-enhanced CT image data sets of lower extremities.

Keywords

References

  1. D. Duncan and N. Ayache, "Medical Image Analysis: Progress over Two Decades and the Challenges Ahead," IEEE Trans. on the Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 85-106, January 2000 https://doi.org/10.1109/34.824822
  2. R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. on the Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647, June 1994 https://doi.org/10.1109/34.295913
  3. T.Kapur, W. Grimson, W. Wells. III, and R. Kinis, "Segmentation of brain tissue from magnetic resonance images," Medical Image Analysis, vol. 1, no. 2, pp. 109-127, 1996 https://doi.org/10.1016/S1361-8415(96)80008-9
  4. A. Elmoutaouakkil, F. Peyrin, J. Elkafi, A. Laval-Jeantet, "Segmentation of cancellous bone from high-resolution computed tomography images: influence on trabecular bone measurements," IEEE Trans. on medical imaging, vol. 21, no. 4, pp. 354-362, December 1996 https://doi.org/10.1109/TMI.2002.1000259
  5. M.Kass, A. Witkin, and D. Terzopoulos, "Snakes: active contour models," International Journal of Computer Vision, vol. 1, no. 3, pp. 312-331, 1988
  6. L.Cohen and I. Cohen, "Finite-element methods for active contour models and balloons for 2-d and 3-d images," IEEE Trans. on the Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1,131-1,147, November 1993
  7. T.B. Sebastian, H. Tek, J.J. Crisco, S.W. Wolfe, and B.B. Kimia, "Segmentation of carpal bones from 3D CT images using skeletally coupled deformable models," MICCAI'98, LNCS 1496, pp1184-1194, 1998
  8. W.Barrett and N. Mortensen, "Interactive live-wire boundary extraction," Medical Image Analysis, vol. 1, no. 4, pp. 331-341, September 1997 https://doi.org/10.1016/S1361-8415(97)85005-0
  9. C.Chu and J.K. Aggarwal, "The integration of image segmentation maps using region and edge information," IEEE Trans. on the Pattern Analysis and Machine Intelligence, vol. 15, no. 12, pp. 1241-1252, December 1993 https://doi.org/10.1109/34.250843
  10. A. Chakraborty, L. Staib, and J. Duncan, "Deformable boundary finding in medical images by integrating gradient and region information," IEEE Trans. on medical imaging, vol. 15, no. 6, pp. 859-870, December 1996 https://doi.org/10.1109/42.544503
  11. M.E. Leventon, W.E.L. Grimson, and O. Faugeras, "Statistical shape Influence in Geodesic Active Contours," Proc. Computer Vision and Pattern Recognition (CVPR) pp. 316-323, 2000
  12. X. Zeng, L.H. Staib, R.T. Shultz and J.S. Duncan, "Segmentation and measurement of the cortex from 3-D MR images using coupled-surface propagation," IEEE Trans. Med. Imag., vol. 18, pp. 927-937, Oct. 1999 https://doi.org/10.1109/42.811276
  13. M. Xu, P.M. Thompson, and A.W. Toga, "An adaptive level set segmentation on a triangulated mesh," IEEE Trans. Med. Imag., vol. 23, pp. 191-201, Feb. 2004 https://doi.org/10.1109/TMI.2003.822823
  14. J. Yang, H. Staib, and J.S. Duncan, "Neighbor-constrained segmentation with level set based 3-D deformable models." IEEE Trans. Med. Imag., vol. 23, pp. 940-948, Aug. 2004 https://doi.org/10.1109/TMI.2004.830802
  15. A. Tsai, A. Yezzi, W. Wells, C. Tempany, D. Tucker, A. Fan, E. Grimson, and A. Willsky, "A shape based approach to curve evolution for segmentation of medical imagery," IEEE Trans. Med. Imag., vol. 22, no. 2, Feb. 2003
  16. M.B. Cuadra, C. Pollo, A. Bardera, O. Cuisenarie, J.G. Villemure, and J.P. Thiran, "Atlas-based segmentation of pathological MR brain images using a model of lesion growth," IEEE Trans. Med. Imag., vol. 23, pp.1301-1314, Oct. 2004 https://doi.org/10.1109/TMI.2004.834618
  17. J. Ehrhardt, H. Handels, T. Malina, B. Strathmann, W. Plotz, S.J. Poppl, "Atlas-based segmentation of bone structures to support the virtual planning of hip operations," International Journal of Medical Informatics, vol. 64, pp439-447, 2001 https://doi.org/10.1016/S1386-5056(01)00212-X
  18. T. Cootes and C. Taylor, "Statistical models of appearance for medical image analysis and computer vision," Proc. SPIE medical Imaging 2001, vol. 4322, pp 236-248, Jul. 2001
  19. D. Freedman, R.J. Radke, T. Zhang, Y. Jeong, D.M. Lovelock, and G. T.Y. Chen, "Model-based segmentation of medical imagery by matching distributions," IEEE Trans. Med. Imag., vol. 24, pp.281-292, Mar. 2005 https://doi.org/10.1109/TMI.2004.841228
  20. K.R. Castleman, Digital Image Processing. Upper Saddle River NJ: Prentice Hall, 1996