Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused
by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments
show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.
Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation
