Automatic three-label bone segmentation from knee MR images

Authors:
Liang Shan;Christopher Zach;Marc Niethammer
Affiliations:
Department of Computer Science, University of North Carolina, Chapel Hill, NC;Department of Computer Science, ETH Zürich, Switzerland;Department of Computer Science, University of North Carolina, Chapel Hill, NC
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

We propose a novel fully automatic three-label bone segmentation approach applied to knee segmentation (femur and tibia) from T1 and T2* magnetic resonance (MR) images. The three-label segmentation approach guarantees separate segmentations of femur and tibia which cannot be assured by general binary segmentation methods. The proposed approach is based on a convex optimization problem by embedding label assignment into higher dimensions. Appearance information is used in the segmentation to favor the segmentation of the cortical bone. We validate the proposed three-label segmentation method on nine knee MR images against manual segmentations for femur and tibia.