膝关节MRI图像中骨骼的精确分割是进一步分割与定量分析膝部软组织的前提。目前膝关节骨骼分割的方法比较耗时或需要一定的人机交互。为解决这一问题，将多尺度MRF方法引入到膝关节MRI分割中，以实现快速无监督的分割。首先建立高斯混合的灰度统计模型，运用MDL准则自动确定类别的数目。建立多尺度MRF的先验模型时，利用尺度间的因果性给出非迭代的计算方法，由细尺度往粗尺度传递统计信息，再由粗尺度往细尺度计算每个像素的最大后验概率，从而实现快速准确的分割。实验结果表明，与单尺度MRF相比，多尺度MRF分割膝关节MRI所需时间大大减少，且精度与专家手动分割标准相当。算法通过建立多尺度马尔可夫随机场模型，完成了低信噪比膝关节MRI图像快速准确分割，可作为进一步自动分割软骨与半月板等软组织的基础。

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.