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
On-line přístup:	Získat plný text
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