A new 3D multi-object segmentation method in cardiac MSCT imaging using a multi-agent approach

IFMBE Proceedings

Volumn 18, Issue , 2008, Pages 249-253

  Fleureau, Julien ^a,b   Garreau, M ^a,b   Boulmier, D ^a,c   Hernandez A ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE RENNES 1   (France)

^c PONTCHAILLOU UNIVERSITY HOSPITAL   (France)

Author keywords

3D segmentation; Cardiac imaging; MSCT; Multi agent system; Supervised classification

Indexed keywords

BIOMEDICAL ENGINEERING; COMPUTERIZED TOMOGRAPHY; COOPERATIVE COMMUNICATION; HOSPITAL DATA PROCESSING; MULTI AGENT SYSTEMS;

3D SEGMENTATION; CARDIAC IMAGING; COMPETITIVE INTERACTIONS; MSCT; MULTI-AGENT APPROACH; MULTI-OBJECT SEGMENTATION; MULTISLICE COMPUTED TOMOGRAPHY; SUPERVISED CLASSIFICATION;

IMAGE SEGMENTATION;

EID: 71649114962     PISSN: 16800737     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-74471-9_58     Document Type: Conference Paper

References (11)

1. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1124-1137, 2004.
2. E. Duchesnay, J.J. Montois, and Y. Jacquelet. Cooperative agents society organized as an irregular pyramid: A mammography segmentation application. Pattern Recognition Letters, 24(14):2435-2445, 2003.
3. A. Falcao, J. Udupa, S. Samarasekera, and S. Sharma. Usersteered image segmentation paradigms: Livewire and livelane. Graphical Models and Image Processing, 60:233-260, 1998.
4. A.F. Frangi, W.J. Niessen, and M.A. Viergever. Threedimensional modeling for functional analysis of cardiac images: A review. IEEE Trans on Medical Imaging, 20(1):2-25, 2001.
5. S. Osher and J.A. Sethian. Fronts propagating with curvature dependent speed: Algorithm based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79:12-49, 1988.
6. N. Richard, M. Dojat, and C. Garbay. Automated segmentation ofhuman brain MR images using a multi-agent approach. Artificial Intelligence in Medicine, 30(2):153-176, 2004.
7. J.K. Udupa, P.K. Saha, and R.A. Lotufo. Relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 24(11):1485-1500, 2002.
8. M. Sermesant, C. Forest, X. Pennec, H. Delingette, and N. Ayache. Deformable biomechanical models: application to 4D cardiac image analysis. Medical Image Analysis, 7(4):475-488, 2003.
9. Y. Y. Boykov and M. P. Jolly. Interactive graph cuts for optimalboundary & region segmentation of objects in N-D images. ComputerVision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 1:105-112, 2001.
10. Gabor T. Herman and Bruno M. Carvalho. Multiseeded segmentation using fuzzy connectedness. IEEE Trans. Pattern Anal. Mach. Intell., 23(5):460-474, 2001.
11. M. Lorenzo-Valdes, G.I. Sanchez-Ortiz, A.G. Elkinton, R.H. Mohiaddin, and D. Rueckert. Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Medical Image Analysis, 8(3):255-265, 2004.