@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}, }