An oriented flux symmetry based active contour model for three dimensional vessel segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6313 LNCS, Issue PART 3, 2010, Pages 720-734

  Law, Max W K ^a   Chung, Albert C S ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DIGITAL STORAGE;

ACTIVE CONTOUR MODEL; CURVILINEAR STRUCTURES; IMAGE GRADIENTS; INTENSITY CHANGE; INTENSITY FLUCTUATIONS; OBJECT BOUNDARIES; SEGMENTATION RESULTS; VESSEL SEGMENTATION;

IMAGE SEGMENTATION;

EID: 78149308415     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15558-1_52     Document Type: Conference Paper

References (15)

1. Caselles, V., et al.: Geodesic active contours. IJCV 22(1), 61-79 (1997)
2. Chan, T., Vese, L.: Active contours without edges. TIP 10(2), 266-277 (2001)
3. Descoteaux, M., Collins, L., Siddiqi, K.: Geometric flows for segmenting vasculature in MRI: Theory and validation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 500-507. Springer, Heidelberg (2004)
4. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, p. 130. Springer, Heidelberg (1998)
5. Gooya, A., Liao, H., Matsumiya, K., Masamune, K., Dohi, T.: Effective statistical edge integration using a flux maximizing scheme for volumetric vascular segmentation in MRA. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 86-97. Springer, Heidelberg (2007)
6. Koller, T., et al.: Multiscale detection of curvilinear structures in 2-d and 3-d image data. In: ICCV, pp. 864-869 (1995)
7. Law, M., Chung, A.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 368-382. Springer, Heidelberg (2008)
8. Lorigo, L., et al.: Curves: Curve evolution for vessel segmentation. MedIA 5(3), 195-206 (2001) (Pubitemid 35282804)
9. Osher, S., Sethian, J.: Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulations. J. Comp. Phys. 79(1), 12-49 (1988)
10. Sato, Y., et al.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. MedIA 2(2), 143-168 (1998) (Pubitemid 128501135)
11. Toledo, R., et al.: Eigensnakes for vessel segmentation in angiography. In: ICPR, pp. 4340-4343 (2000)
12. Vasilevskiy, A., Siddiqi, K.: Flux maximizing geometric flows. PAMI 24(12), 1565-1578 (2002)
13. Whitaker, R.: A level-set approach to 3d reconstruction from range data. IJCV 29(33), 203-231 (1998)
14. Yan, P., Kassim, A.: Segmentation of volumetric mra images by using capillary active contour. MedIA 10(3), 317-329 (2006)
15. Lesage, D., et al.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. MedIA 13(6), 819-845 (2009)