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Applied Computational Intelligence and Soft Computing
Volume 2011 (2011), Article ID 786369, 11 pages
A New Framework of Multiphase Segmentation and Its Application to Partial Volume Segmentation
1Department of Mathematics, University of Florida, Gainesville, FL 32611-8105, USA
2Department of Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA
Received 14 October 2010; Revised 24 January 2011; Accepted 16 February 2011
Academic Editor: Antonio Di Nola
Copyright © 2011 Fuhua Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. K. Zhao, T. Chan, B. Merriman, and S. Osher, “A variational level set approach to multiphase motion,” Journal of Computational Physics, vol. 127, no. 1, pp. 179–195, 1996.
- L. A. Vese and T. F. Chan, “A multiphase level set framework for image segmentation using the Mumford and Shah model,” International Journal of Computer Vision, vol. 50, no. 3, pp. 271–293, 2002.
- J. Lie, M. Lysaker, and X. C. Tai, “A variant of the level set method and applications to image segmentation,” Mathematics of Computation, vol. 75, no. 255, pp. 1155–1174, 2006.
- G. Chung and L. A. Vese, “Energy minimization based segmentation and denoising using a multilayer level set approach,” in Energy Minimization Methodsin Computer Vision and Pattern Recognition, vol. 3757 of Lecture Notes in Computer Science, pp. 439–455, 2005.
- S. R. Thiruvenkadam, S. Arcot, and Y. Chen, “A PDE based method for fuzzy classification of medical images,” in Proceedings of the International Conference on Image Processing (ICIP '06), vol. 481, pp. 1805–1808, 2006.
- J. Shen, “A stochastic-variational model for soft Mumford-Shah segmentation,” International Journal of Biomedical Imaging, vol. 2006, Article ID 92329, 14 pages, 2006.
- S. Esedoglu and Y. H. R. Tsai, “Threshold dynamics for the piecewise constant Mumford-Shah functional,” Journal of Computational Physics, vol. 211, no. 1, pp. 367–384, 2006.
- J. W. Cahn and J. E. Hilliard, “Free energy of a nonuniform system. I. Interfacial free energy,” The Journal of Chemical Physics, vol. 28, no. 2, pp. 258–267, 1958.
- L. Modica and S. Mortola, “Un esempio di -convergenza,” Bollettino della Unione Matematica Italiana, vol. 14, pp. 285–299, 1977.
- L. Modica, “The gradient theory of phase transitions and the minimal interface criterion,” Archive for Rational Mechanics and Analysis, vol. 98, no. 2, pp. 123–142, 1987.
- B. Bourdin and A. Chambolle, “Design-dependent loads in topology optimization,” ESAIM Control, Optimisation and Calculus of Variations, no. 9, pp. 19–48, 2003.
- M. Burger and R. Stainko, “Phase-field relaxation of topology optimization with local stress constraints,” SIAM Journal on Control and Optimization, vol. 45, no. 4, pp. 1447–1466, 2006.
- M. Y. Wang and S. Zhou, “A variational method for structural topology optimization,” Computer Modeling in Engineering and Sciences, vol. 6, no. 6, pp. 547–566, 2004.
- M. Rumpf and B. Wirth, “A nonlinear elastic shape averaging approach,” SIAM Journal on Imaging Sciences, vol. 2, pp. 800–833, 2009.
- J. H. An and Y. Chen, “Region based image segmentation using modified Mumford-Shah algorithm,” in Proceedings of the 1st International Conference on Scale Space and Variation Variational Methods in Computer Vision, vol. 4485, pp. 733–742, 2007.
- F. Chen, Y. Chen, and H. D. Tagare, “An extension of sine-sinc model based on logarithm of likelihood,” in Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV '08), vol. 1, pp. 222–227, 2008.
- Y. M. Jung, S. H. Kang, and J. Shen, “Multiphase image segmentation via Modica-Mortola phase transition,” SIAM Journal on Applied Mathematics, vol. 67, no. 5, pp. 1213–1232, 2007.
- Y. Boykov and G. Funka-Lea, “Graph cuts and efficient N-D image segmentation,” International Journal of Computer Vision, vol. 70, no. 2, pp. 109–131, 2006.
- X. Bresson and T. F. Chan, “Non-local unsupervised variational image segmentation model,” UCLA CAM Report 08-67, 2008.
- C. Vogel, Computational Methods for Inverse Problems, SIAM, Philadelphia, Pa, USA, 2002.
- A. L. Yuille and A. Rangarajan, “The concave-convex procedure,” Neural Computation, vol. 15, no. 4, pp. 915–936, 2003.
- K. V. Leeput, F. Maes, D. Vandermeulen, and P. Suetens, “A unifying frame-work for partial volume segmentation of brain MR images,” IEEE Transactions on Medical Imaging, vol. 22, pp. 105–119, 2003.
- W. J. Niessen, K. L. Vincken, J. Weickert, B. M. Ter Haar Romeny, and M. A. Viergever, “Multiscale segmentation of three-dimensional MR brain images,” International Journal of Computer Vision, vol. 31, no. 2, pp. 185–202, 1999.
- D. Eremina, X. Li, W. Zhu, J. Wang, and Z. Liang, “Investigation on an EM framework for partial volume image segmentation,” in Medical Imaging: Image Processing, vol. 6144 of Proceedings of SPIE, pp. 1–9, February 2006.
- H. D. Targare, Y. Chen, and R. K. Fulbright, “A reparameterized level set algorithm and its comparison with EM-based partial volume segmentation of MR brain images,” SPIE 2008.
- D. L. Pham and J. L. Prince, “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognition Letters, vol. 20, no. 1, pp. 57–68, 1999.