SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
A Bayesian multilevel model for fMRI data analysis
Computer Methods and Programs in Biomedicine
Bayesian segmentation of magnetic resonance images using the α-stable distribution
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution
Computers in Biology and Medicine
Gaussian mixture model based segmentation methods for brain MRI images
Artificial Intelligence Review
| Hi-index | 0.00 |
We present Bayesian methodologies and apply Markov chain sampling techniques for exploring normal mixture models with an unknown number of components in the context of magnetic resonance imaging (MRI) segmentation. The experiments show that by estimating the number of components using sample-based approaches based on variable dimension models the discriminating power of the estimated components is improved. Two different MCMC methods are compared to perform the segmentation of simulated magnetic resonance brain scans, the reversible jump MCMC model and the Dirichlet process (DP) mixture model. The preference given to the Dirichlet process mixture model is discussed.