Semi Supervised Color Image Segmentation Using Region Based Method
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - May Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-5 | ||
| Year of Publication : 2013 | ||
| Authors :M.MargretRubini, Dr.P.Eswaran |
M.MargretRubini, Dr.P.Eswaran"Semi Supervised Color Image Segmentation Using Region Based Method "International Journal of Computer Trends and Technology (IJCTT),V4(5):1376-1382 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - We propose a novel region-based method for image segmentation, which is able to deal with intensity in homogeneities in the segmentation. First, based on the model of images with intensity in homogeneities, we derive a local intensity clustering property of the image intensities, and de?ne a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion de?nes an energy in terms of the level set functions that represent a partition of the image domain and a bias ?eld that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias ?eld, and the estimated bias ?eld can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results. .
References-
[1] G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. New York: Springer-Verlag, 2002.
[2] V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numer. Math., vol. 66, no. 1, pp. 1–31, Dec. 1993.
[3] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb. 1997.
[4] T. Chan and L. Vese, “Active contours without edges,” IEEE Trans. Image. Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.
[5] D. Cremers, “A multiphase levelset framework for variational motion segmentation,” in Proc. Scale Space Meth. Comput. Vis., Isle of Skye, U.K., Jun. 2003, pp. 599– 614.
[6] L. Evans, Partial Differential Equations. Providence, RI: Amer. Math. Soc., 1998.
[7] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Gradient flows and geometric active contour models,” in Proc. 5th Int. Conf. Comput. Vis., 1995, pp. 810–815.
[8] R. Kimmel, A. Amir, and A. Bruckstein, “Finding shortest paths on surfaces using level set propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 6, pp. 635–640, Jun. 1995.
[9] C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, “A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity,” in Proc. Med. Image
[10] Comput. Comput. Aided Intervention, 2008, vol. LNCS 5242, pp. 1083–1091, Part II.
Keywords — Region-based method, Edge-based method, Level-set method, Segmentation.