A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

A new semi-supervised clustering technique using multi-objective optimization

(2015) Abhay Kumar Alok et al.   APPLIED INTELLIGENCE

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

(2015) Long Thanh Ngo et al.   COMPUTERS & GEOSCIENCES

Fuzzy mathematical morphology for biological image segmentation

(2014) Laura Caponetti et al.   APPLIED INTELLIGENCE

Evaluation of Current Dental Radiographs Segmentation Approaches in Computer-aided Applications

(2013) AbdolvahabEhsani Rad et al.   IETE TECHNICAL REVIEW

Semi-supervised fuzzy clustering with metric learning and entropy regularization

(2012) Xuesong Yin et al.   KNOWLEDGE-BASED SYSTEMS

Characteristic analysis of Otsu threshold and its applications

(2011) Xiangyang Xu et al.   PATTERN RECOGNITION LETTERS

Novel modified fuzzy c-means algorithm with applications

(2007) Jiayin Kang et al.   DIGITAL SIGNAL PROCESSING