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Multi-classifier framework for atlas-based image segmentation

โœ Scribed by Torsten Rohlfing; Calvin R. Maurer Jr.

Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
235 KB
Volume
26
Category
Article
ISSN
0167-8655

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โœฆ Synopsis

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.

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