Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images

We propose a fully automatic method for segmenting knee cartilage in 3-D MR images which consists of bone segmentation, bone-cartilage interface (BCI) classification, and cartilage segmentation. For bone segmentation, we propose a modified version of the recently presented branch-and-mincut method, and for classifying the BCI, we propose a voxel classification method based on binary classifiers of position and local appearance. The core contribution of this paper is the cartilage segmentation method where localized Markov random fields (MRF) are separately constructed and optimized for local image patches. The region and boundary potentials of the MRFs are computed from the retrieved segmentation results of training images that are relevant to each local patch. Here, local shape and appearance cues are adaptively combined depending on the local image characteristics. For experimentation, a dataset comprising MR images of ten different subjects and another comprising the baseline and two-year follow-up scans for nine different subjects are constructed. Both qualitative and quantitative comparisons of the results of the proposed method with semi-automatic segmentation methods demonstrate the potential of the proposed method for clinical application.