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This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against 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 78.2% and 82.6% respectively).