A new hybrid algorithm for image segmentation based on rough sets and enhanced fuzzy c-means clustering
Wei Zhang, Cheng Li, Yuzhu Zhang · 2009
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Both fuzzy set and rough set provide a mathematical framework to capture uncertainties associated with human cognition process. The enhanced fuzzy c-means algorithm (EnFCM) can speed up the segmentation process for gray-level image, especially for MR image segmentation. In this paper, an improved hybrid algorithm called rough- enhanced fuzzy c-means (REnFCM) algorithm is presented for segmentation of brain MR images. The experimental results indicate that the proposed algorithm is more robust to the noises and faster than many other segmentation algorithms.