Abstract
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.
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Shi, Y., Qi, F. (2004). Adaptive Stereo Brain Images Segmentation Based on the Weak Membrane Model. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_94
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DOI: https://doi.org/10.1007/978-3-540-30497-5_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
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