Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture...

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Vydáno v:IEEE transactions on medical imaging Ročník 25; číslo 9; s. 1233 - 1245
Hlavní autoři: Greenspan, H., Ruf, A., Goldberger, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Alzheimer's disease
Artificial Intelligence
Brain
Brain - anatomy & histology
Brain modeling
Constrained model
expectation-maximization (EM)
Gaussian mixture model (GMM)
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Information Storage and Retrieval - methods
Jacobian matrices
Magnetic liquids
Magnetic noise
Magnetic resonance
Magnetic resonance imaging
magnetic resonance imaging (MRI) brain segmentation
Magnetic Resonance Imaging - methods
mixture of Gaussians
Models, Neurological
Models, Statistical
Noise reduction
Normal Distribution
Pattern Recognition, Automated - methods
Pediatrics
Reproducibility of Results
Sensitivity and Specificity
Studies
ISSN:0278-0062, 1558-254X
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Popis
Shrnutí:An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains
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ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.880668