Fuzzy region competition: A convex two-phase segmentation framework

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

Volumn 4485 LNCS, Issue , 2007, Pages 214-226

  Mory, Benoit ^a   Ardon, Roberto ^a  

^a PHILIPS HEALTHCARE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; IMAGE SEGMENTATION; MATHEMATICAL MODELS; MEDICAL APPLICATIONS; PROBLEM SOLVING; THREE DIMENSIONAL;

CLASSICAL FORMULATIONS; FUZZY REGION COMPETITION; THRESHOLDING;

FUZZY SETS;

EID: 37249052940     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72823-8_19     Document Type: Conference Paper

References (17)

1. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher. Global minimizers of the active contour/snake model. No CAM Report 0504, UCLA, Dept. of Mathematics, 2005.
2. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher. Fast global minimization of the active contour/snake model, to appear in Journal of Mathematical Imaging and Vision, 2006.
3. A. Chambolle. An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision, 20(1-2):89-97, 2004.
4. T.F. Chan, S. Esedoglu, and M. Nikolova. Algorithms for finding global minimizers of image segmentation and denoising models. UCLA CAM Report 04-54, 2004.
5. T.F. Chan and L.A. Vese. Active contours without edges. IEEE Trans. on Image Processing, 10(2):266-277, February 2001.
6. W. Fleming and R. Rishel. An integral formula for total gradient variation. Archiv Der Mathematik, 11:218-222, 1960.
7. S. Jehan-Besson, M. Barlaud, and G. Aubert. Dream2s: Deformable regions driven by an eulerian accurate minimization method for image and video segmentation. Int. Journal of Computer Vision, 53(1):45-70, 2003.
8. H. Knutsson and C.F. Westin. Normalized and differential convolution: Methods for interpolation and filtering of incomplete and uncertain data. In Proc. of Computer Vision and Pattern Recognition, pages 515-523, New York City, USA, June 16-19 1993.
9. D. Mumford and J. Shah. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. on Pure and Applied Mathematics, 42(5):577-685, 1989.
10. S. Osher and J.A. Sethian. Fronts propagating with curvature dependent speed: algorithms based on the hamilton-jacobi formulation. Journal of Computational Physics, 79:12-49, 1988.
11. N. Paragios and N. Deriche. Geodesic active regions and level set methods for supervised texture segmentation. Int. Journal of Computer Vision, 46(3):223-247, 2002.
12. Jianhong (Jackie) Shen. A stochastic-variational model for soft mumford-shah segmentation. International Journal of Biomedical Imaging, 2006(92329).
13. A. Tsai, A. Yezzi Jr., and A.S. Willsky. Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. on Image Processing, 10(8).1169-1186, 2001.
14. L.A. Vese and T.F. Chan. A multiphase level set framework for image segmentation using the mumford and shah model. Int. Journal of Computer Vision, 50(3):271-293, 2002.
15. I. Young and L. van Vliet. Recursive implementation of the gaussian filter. Signal Processing, 44:139-151, 1995.
16. H. K. Zhao, T. Chan, B. Merriman, and S. Osher. A variational level set approach to multiphase motion. Journal of Computational Physics, 127:179-195, 1996.
17. S. C. Zhu and A. Yuille. Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. On Pattern Analysis and Machine Intelligence, 18(9):884-900, September 1996.