Computing average shaped tissue probability templates

This note presents a framework for generating tissue probability maps that represent the average shape of a number of subjects' brain images. The procedure is formulated as finding maximum a posteriori estimates within a probabilistic generative model. Estimating the parameters involves alterna...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 45; H. 2; S. 333 - 341
Hauptverfasser: Ashburner, John, Friston, Karl J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.04.2009
Elsevier Limited
Schlagworte:
Algorithms
Brain
Brain - anatomy & histology
Computer Simulation
Data Interpretation, Statistical
Deformation
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Models, Biological
Models, Statistical
Pattern Recognition, Automated - methods
Probability
Reproducibility of Results
Sensitivity and Specificity
ISSN:1053-8119, 1095-9572, 1095-9572
Online-Zugang:Volltext
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Zusammenfassung:This note presents a framework for generating tissue probability maps that represent the average shape of a number of subjects' brain images. The procedure is formulated as finding maximum a posteriori estimates within a probabilistic generative model. Estimating the parameters involves alternating between estimating the deformations that match tissue class images of individual subjects to template, and updating the template according to the latest estimates of the deformations. A multinomial matching criterion is used, such that multiple tissue class images (e.g. grey and white matter) are registered simultaneously with the current template estimate. In order to generalise the resulting template to a broader range of subjects, a template blurriness prior is included within the model.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2008.12.008