Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 02001
Number of page(s) 7
Section Session 2: Bioinformatics
DOI https://doi.org/10.1051/itmconf/20171202001
Published online 05 September 2017
  1. M. Kass, A. Witriw, and D. Terzopoilos, “Snakes: active contour models,” International Journal of Computer Vision, 1988, 1:321–369 [CrossRef] [Google Scholar]
  2. V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathmatik., 1993, 66:1–31. [CrossRef] [Google Scholar]
  3. T. Chan, and L. Vese, “Active contours without edge,” IEEE Transactions on Image Processing, 2001, 10(2):266–277. [NASA ADS] [CrossRef] [Google Scholar]
  4. C. Li, C. Kao, C. Gore, and Z. Ding, “Implicit active contours driven by local binary fitting energy,” IEEE Computer Vision and Pattern Recognition, 2007, 6:17–22. [Google Scholar]
  5. C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization; a new variational formulation,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1:430–436 [Google Scholar]
  6. D. Gabor, “Information theory in electron microscopy,” Laboratory Investigation, 1965, 14:801–807. [Google Scholar]
  7. A. K. Jain, “Partial differential equations and finite-difference methods in image processing, part 1: Image representation,” Optimization Theory and Applications, 1997, 23: 65–91. [CrossRef] [Google Scholar]
  8. J. J. koenderink, “The structure of images,” Biological Cybernetics, 1984, 50:363–370. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  9. A. P. Witkin, “Scale-space filtering,” Proceedings of the 8th International Joint Conference Artificial Intelligence (IJCAI), Karlsruhe Germany, 1983, 2:1019–1021. [Google Scholar]
  10. L. Alvarez, F. Guichard, P. L. Lions, and J. M. Morel, et.al. “Axioms and fundamental euations of image processing,” Archive for Rational Mechanics and Analysis, 1993, 16(9):200–257. [Google Scholar]
  11. C. Li, C. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Transactions on Image Processing, 2008, 17(10):1940–1949. [CrossRef] [MathSciNet] [Google Scholar]
  12. L. Wang, C. Li, Q. Sun, D. Xia, and C. Kao, “Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation,” Computerized Medical Imaging and Graphics, 2009, 33:520–531. [CrossRef] [Google Scholar]
  13. B. R. Frieden, “Restoring with maximum likelihood and maximum entropy,” Journal of the Optical Society of America, 1982, 62(4):511–518. [NASA ADS] [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  14. M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, 2004, 13(1):146–165. [CrossRef] [Google Scholar]

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