Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Three-Dimensional MR Brain Segmentation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Convex Optimization
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
Bayesian multichannel image restoration using compound Gauss-Markov random fields
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
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In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is an edge-preserving smoothness prior which is imposed on the probabilities of the voxel labels. This prior incorporates a line process, which is modeled as a Bernoulli random variable, in order to preserve edges between tissues. The main difference with other, state of the art methods imposing priors, is that the constraint is imposed on the probabilities of the voxel labels and not onto the labels themselves. Inference of the proposed Bayesian model is obtained using variational methodology and the model parameters are computed in closed form. Numerical experiments are presented where the proposed model is favorably compared to state of the art brain segmentation methods as well as to a spatially varying Gaussian mixture model.