Robust vessel tree modeling

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

Volumn 5241 LNCS, Issue PART 1, 2008, Pages 602-611

  Gülsün, M Akif ^a   Tek, Hüseyin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CENTERLINES; COMPUTATIONALLY EFFICIENT; CONTRAST-ENHANCED; CORONARY ARTERIES; COST PATH; DATA SETS; DETECTION ALGORITHM; GRAPH-BASED; MULTISCALES; NOVEL METHODS; OPTIMIZATION ALGORITHMS; OPTIMIZATION TECHNIQUES; SALIENCY MEASURE; TREE MODELING;

BLOOD VESSELS; COMPUTATIONAL EFFICIENCY; COMPUTER SCIENCE; COSTS; MEDICAL COMPUTING;

MEDICAL IMAGING;

ALGORITHM; ANGIOGRAPHY; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BLOOD VESSEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ANGIOGRAPHY; ARTIFICIAL INTELLIGENCE; BLOOD VESSELS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84883836755     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85988-8_72     Document Type: Conference Paper

References (12)

1. Aylwaid, S., Eberly, E.: Initialization, noise, singularities, and scale in height-ridge traversal for tubular object centerline extraction. TMI 21(2), 61-75 (2002)
2. Aylward, S., Pizer, S., Bullitt, E., Eberly, D.: Intensity ridge and widths for 3d object segmentation and description. In: IEEE Proc. Workshop MMBIA, pp. 131-138 (1996)
3. Deschamps, T., Cohen, L.: Fast extraction of minimal paths in 3d images and applications to virtual endoscopy. Medical Image Analysis 5(4), 281-299 (2001)
4. Dijkstra, E.W.: A note on two problems in connections with graphs. Numerische Mathematic 1, 269-271 (1959)
5. 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, pp. 82-89. Springer, Heidelberg (1998)
6. Koller, T.M., Gerig, C., Szekely, G., Dettwiler, D.: Multiscale detection of curvilinear structures in 2-D and 3-D image data. In: ICCV, pp. 864-869 (1995)
7. Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based multiscale detection of 3D vessels. In: IEEE Conf. CVPR, pp. 722-727 (1998)
8. Li, H., Yezzi, A.J.: Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Trans. Med. Imaging 26(9), 1213-1223 (2007)
9. Sethian, J.A.: Level Set Methods. Cambridge University Press, New York (1996)
10. Siddiqi, K., Vasilevskiy, A.: 3D flux maximizing flows. In: International Workshop on Energy Minimizing Methods In Computer Vision (2001)
11. Tyrrell, J.A., di Tomaso, E., Fuja, D., Tong, R., Kozak, K., Brown, E.B., Jain, R., Roysam, B.: Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Transactions on Medical Imaging (2006)
12. Wink, O., Niessen, W.J., Viergever, M.A.: Multiscale vessel tracking. IEEE Trans. on Medical Imaging 23(1), 130-133 (2004)