@article{(Open Science Index):https://publications.waset.org/pdf/8313,
	  title     = {Dempster-Shafer Evidence Theory for Image Segmentation: Application in Cells Images},
	  author    = {S. Ben Chaabane and  M. Sayadi and  F. Fnaiech and  E. Brassart},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper we propose a new knowledge model using
the Dempster-Shafer-s evidence theory for image segmentation and
fusion. The proposed method is composed essentially of two steps.
First, mass distributions in Dempster-Shafer theory are obtained from
the membership degrees of each pixel covering the three image
components (R, G and B). Each membership-s degree is determined by
applying Fuzzy C-Means (FCM) clustering to the gray levels of the
three images. Second, the fusion process consists in defining three
discernment frames which are associated with the three images to be
fused, and then combining them to form a new frame of discernment.
The strategy used to define mass distributions in the combined
framework is discussed in detail. The proposed fusion method is
illustrated in the context of image segmentation. Experimental
investigations and comparative studies with the other previous methods
are carried out showing thus the robustness and superiority of the
proposed method in terms of image segmentation.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {11},
	  year      = {2009},
	  pages     = {2648 - 2654},
	  ee        = {https://publications.waset.org/pdf/8313},
	  url   	= {https://publications.waset.org/vol/35},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 35, 2009},