Multi-classifier framework for atlas-based image segmentation

Authors:
Torsten Rohlfing;Calvin R. Maurer, Jr.
Affiliations:
Department of Neurosurgery, Stanford University, 300 Pasteur Drive, MC 5327, Stanford, CA 94305-5327, USA;Department of Neurosurgery, Stanford University, 300 Pasteur Drive, MC 5327, Stanford, CA 94305-5327, USA
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

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.