Multivariate image segmentation using semantic region growing with adaptive edge penalty
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images