@article{(Open Science Index):https://publications.waset.org/pdf/10002261,
	  title     = {Liver Lesion Extraction with Fuzzy Thresholding in Contrast Enhanced Ultrasound Images},
	  author    = {Abder-Rahman Ali and  Adélaïde Albouy-Kissi and  Manuel Grand-Brochier and  Viviane Ladan-Marcus and  Christine Hoeffl and  Claude Marcus and  Antoine Vacavant and  Jean-Yves Boire},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {7},
	  year      = {2015},
	  pages     = {1730 - 1734},
	  ee        = {https://publications.waset.org/pdf/10002261},
	  url   	= {https://publications.waset.org/vol/103},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 103, 2015},