Automatic three-label bone segmentation from knee MR images


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.

Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010
Liang Shan
Liang Shan
Ph.D. in Computer Science

My research interests include image registration for images with pathologies and machine learning.

Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.