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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 8508329, 14 pages
http://dx.doi.org/10.1155/2016/8508329
Research Article

Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation

1School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
2Department of Natural Science & Mathematics, West Liberty University, West Liberty, WV 26074, USA

Received 27 March 2016; Accepted 29 May 2016

Academic Editor: Serafín Moral

Copyright © 2016 Hong-Yuan Wang and Fuhua Chen. 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.

Linked References

  1. F. C. Katherine Hastings, “Volume estimation of various brain components using mr images—a technical report,” Journal of Applied and Computational Mathematics, vol. 4, article 207, 2015. View at Publisher · View at Google Scholar
  2. K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “A unifying framework for partial volume segmentation of brain MR images,” IEEE Transactions on Medical Imaging, vol. 22, no. 1, pp. 105–119, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. 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, San Diego, Calif, USA, February 2006. View at Publisher · View at Google Scholar
  4. H. D. Targare, Y. Chen, and R. K. Fulbright, “Comparison of EM-based and level set partial volume segmentations of MR brain images,” in Proceedings of the Medical Imaging 2008: Image Processing, vol. 6914 of Proceedings of SPIE, pp. 1–7, San Diego, Calif, USA, February 2008. View at Publisher · View at Google Scholar
  5. F. Chen, Y. Chen, and H. D. Tagare, “A new framework of multiphase segmentation and its application to partial volume segmentation,” Applied Computational Intelligence and Soft Computing, vol. 2011, Article ID 786369, 11 pages, 2011. View at Publisher · View at Google Scholar
  6. J. Ashburner and K. J. Friston, “Unified segmentation,” NeuroImage, vol. 26, no. 3, pp. 839–851, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, “A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity,” Medical Image Computing and Computer-Assisted Intervention, vol. 11, part 2, pp. 1083–1091, 2008. View at Google Scholar
  9. J. Zhang, J. W. Modestino, and D. A. Langan, “Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation,” IEEE Transactions on Image Processing, vol. 3, no. 4, pp. 404–420, 1994. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Shen, “A stochastic-variational model for soft mumford-shah segmentation,” International Journal of Biomedical Imaging, vol. 2006, Article ID 92329, 14 pages, 2006. View at Publisher · View at Google Scholar
  11. K. Z. Mao, P. Zhao, and P.-H. Tan, “Supervised learning-based cell image segmentation for p53 immunohistochemistry,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 6, pp. 1153–1163, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Derivaux, G. Forestier, C. Wemmert, and S. Lefvre, “Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation,” Pattern Recognition Letters, vol. 31, no. 15, pp. 2364–2374, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum, “Poisson matting,” ACM Transactions on Graphics, vol. 23, no. 3, pp. 315–321, 2004. View at Google Scholar
  14. K.-S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, and T.-J. Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9–15, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Levin, A. Rav-Acha, and D. Lischinski, “Spectral matting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 10, pp. 1699–1712, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Rother, V. Kolmogorov, and A. Blake, GrabCut—Interactive Foreground Extraction Using Iterated Graph Cuts, Microsoft Research, 2004.
  17. 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 Transactions on Image Processing, vol. 10, no. 8, pp. 1169–1186, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. 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. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Esedoglu and Y.-H. Richard Tsai, “Threshold dynamics for the piece wise constant mumford-shah functiona,” Journal of Computational Physics, 2006. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.4623.
  21. C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, “A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI,” IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 2007–2016, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. Y. Duan, H. Chang, W. Huang, and J. Zhou, “Simultaneous bias correction and image segmentation via L0 regularized Mumford-Shah model,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '14), pp. 6–40, Paris, France, October 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Mumford and J. Shah, “Optimal approximations by piecewise smooth functions and associated variational problems,” Communications on Pure and Applied Mathematics, vol. 42, no. 5, pp. 577–685, 1989. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  24. C. Chen, J. Leng, and G. Xu, “A General Framework of Piecewise-Polynomial Mumford-Shah Model for Image Segmentation,” UCLA cam13-50, 2013. View at Google Scholar
  25. C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Transactions on Image Processing, vol. 17, no. 10, pp. 1940–1949, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. T. L. Huntsherger, C. L. Jacobs, and R. L. Cannon, “Iterative fuzzy image segmentation,” Pattern Recognition, vol. 18, no. 2, pp. 131–138, 1985. View at Publisher · View at Google Scholar · View at Scopus
  27. F. Li, M. K. Ng, and C. Li, “Variational fuzzy Mumford-Shah model for image segmentation,” SIAM Journal on Applied Mathematics, vol. 70, no. 7, pp. 2750–2770, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “A unifying framework for partial volume segmentation of brain MR images,” IEEE Transactions on Medical Imaging, vol. 22, no. 1, pp. 105–119, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. X. Li, L. Li, H. Lu, and Z. Liang, “Partial volume segmentation of brain magnetic resonance images based on maximum a posteriori probability,” Medical Physics, vol. 32, no. 7, pp. 2337–2345, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. F. Chen and K. Hastings, “Volume estimation of various brain components using MR images—a technical report,” Journal of Applied & Computational Mathematics, vol. 4, no. 2, article 207, 2015. View at Publisher · View at Google Scholar
  31. W.-C. Chiu and M. Fritz, Multi-Class Video Co-Segmentation with a Generative Multi-Video Model, https://scalable.mpi-inf.mpg.de/files/2013/04/chiu13cvpr.pdf.
  32. Y. Wang, Y. Li, and Q. Zhao, “Segmentation of high-resolution SAR image with unknown number of classes based on regular tessellation and RJMCMC algorithm,” International Journal of Remote Sensing, vol. 36, no. 5, pp. 1290–1306, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Computer Graphics and Image Processing, vol. 9, no. 4, pp. 394–407, 1979. View at Publisher · View at Google Scholar · View at Scopus
  34. M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 421–432, 1998. View at Publisher · View at Google Scholar · View at Scopus
  35. F. Chen and Y. Chen, “A stochastic variational model for multi-phase soft segmentation with bias correction,” Advanced Modeling and Optimization, vol. 12, no. 3, pp. 339–345, 2010. View at Google Scholar · View at MathSciNet
  36. M. Zhu and T. Chan, “An efficient primal-dual hybrid gradient algorithm for total variation image restoration,” CAM Report cam08-34, 2008. View at Google Scholar