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.

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Torsten Rohlfing, Calvin R. Maurer Jr.
• Keywords: Atlas-based Segmentation, Biomedical Image Segmentation, Computer Vision, CVPR 2004, Ground Truth Segmentation, Increase Classification Accuracy, Real-world Segmentation Problems