Akademik Veri Yönetim Sistemi
ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH, cilt.29, sa.2, ss.715-720, 2012 (SCI-Expanded)
A new image segmentation approach is proposed. The proposed method eliminates these numerical errors in few iteration steps using fuzzy clustering for fast CT liver tissue segmentation. Spatial fuzzy clusters determine the initial contour to reduce the iteration steps. Fuzzy c-means used for spatial fuzzy clustering and applied on distance regularized level set evolution for fast CT liver tissue segmentation, which is called distance regularized level set evolution based on spatial fuzzy clustering (DRLSESFC) in CT liver tissue segmentation. DRLSESFC provides fast evolution process for object detection in CT liver tissue segmentation. This method enjoys higher speed, less processing time and more answer's optimum. Also the proposed method has a better object detection than other level set methods.