A Generative Model for Image Segmentation Based on Label Fusion

We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 29; H. 10; S. 1714 - 1729
Hauptverfasser:	Sabuncu, Mert R, Yeo, B T Thomas, Van Leemput, K, Fischl, Bruce, Golland, Polina
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Aging Algorithms Biomedical imaging Brain Brain - anatomy & histology Cerebral cortex Computer Simulation Fusion power generation Humans Image Enhancement - methods Image generation Image Interpretation, Computer-Assisted - methods Image parcellation image registration Image segmentation Inference algorithms Magnetic resonance imaging Magnetic Resonance Imaging - methods Models, Anatomic Models, Neurological Pattern Recognition, Automated - methods Reproducibility of Results Robustness Sensitivity and Specificity Studies Subtraction Technique Testing
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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