Segmentation of textured images using Gibbs random fields
Computer Vision, Graphics, and Image Processing
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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A generic algorithm is presented for the segmentation of three-dimensional multispectral magnetic resonance images. The algorithm is unsupervised and adaptive, does not require initialization, classifies the data in any number of tissue classes and suggests an optimal number of classes. It uses a statistical model including Bayesian distributions for brain tissues intensities and Gibbs Random Fields (GRF)-based spatial contiguity constraints. The classification is unsupervised, that is to say the intensity-based signatures of brain tissues and the spatial hyperparameters of the underlying GRF are derived from the data. Adaptivity is achieved through the variation of the size of the neighborhoods used for the estimation of the intensity characteristics. This allows slow variations of signal intensity in space to account for MRI intensity nonuniformity. Segmentation results with proton density, T2 and T1-weighted data are provided. The algorithm can be used as an independent segmentation module within a brain MRI data processing pipeline.