Markov random field modeling in computer vision
Markov random field modeling in computer vision
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Anisotropic Parameter Estimation in the Weak Membrane Model
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
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This paper presents a new method for automatically segmenting brain parenchyma and cerebrospinal fluid in routine single-echo magnetic resonance (MR) images. Our method is based on the weak membrane model. Weak membrane models can model intensity measurement at each voxel site to implement piecewise smoothness constraint, and at the same time model discontinuities to control the interaction between each pair of the neighboring pixel. Segmentation is obtained by seeking for the maximum a posteriori estimation of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields (MRFs) or Gibbs random fields (GRFs) models. Our approach has the following desirable properties: (1) brain voxels can be accurately classified into white matter, grey matter and cerebrospinal fluid (CSF), and (2) relatively insensitive to noise and intensity inhomogeneity.