Abstract
In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification. Validation results on 18 knee MR images against the manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 75.2% and 81.7% respectively).
Liang Shan
Ph.D. in Computer Science
My research interests include image registration for images with pathologies and machine learning.
Marc Niethammer
Professor of Computer Science
My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.