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Multi-Classifier Framework for Atlas-Based Image Segmentation

10 years 9 days ago
Multi-Classifier Framework for Atlas-Based Image Segmentation
Three different systematic approaches to generate multiple classifiers in atlas-based biomedical image segmentation are compared. Different atlases, as well as different parametrization of the registration algorithm, lead to different atlasbased classifiers. The classifier outputs are combined and compared to a manual ground truth segmentation. Classifier combination consistently improved classification accuracy with the biggest improvement from multiple atlases. We conclude that multi-classifier techniques have a natural application to atlas-based segmentation and increase classification accuracy in real-world segmentation problems. ? 2005 Elsevier B.V. All rights reserved.
Torsten Rohlfing, Calvin R. Maurer Jr.
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Torsten Rohlfing, Calvin R. Maurer Jr.
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