Bone segmentation in knee MRI can be regarded as the groundwork of segmenting and analyzing soft tissue in knees. Usually this task is time-consuming and needs human intervention. To solve this problem automatically and rapidly

a multi-scale MRF is introduced into knee MRI segmentation in this paper. Gaussian mixture model is firstly built as the statistical model for the intensity image

with an estimation of index number using MDL. In the phase of building multi-scale MRF model

non-iterated computing based on causality between scales is implemented

where statistical information is transferred from fine scales to coarse scales and MAP of every pixel is computed from coarse scales to fine scales. As a result

fast and unsupervised bone segmentation on knee MRI can be achieved. The experiments show that the temporal cost of segmenting knee bones based on multi-scale MRF is extremely low and the segmentation error can be comparable to manual segmentation by medical experts. In conclusion

the work presented here accomplishes fast and accurate segmentation on knee MRI of low SNR through building a multi-scale MRF model. Future work can be extended to further cartilage and meniscus segmentation.