Multi-classifier framework for atlas-based image segmentation

doi:10.1016/j.patrec.2005.03.017

Multi-classifier framework for atlas-based image segmentation

2005-10-01

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DOI：10.1016/j.patrec.2005.03.017

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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 atlas-based 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. (c) 2005 Elsevier B.V. All rights reserved.

Keywords:

atlas-based segmentation

multiple classifier system

bagging

non-rigid registration

Multi-classifier framework for atlas-based image segmentation

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