Locating articular cartilage in MR images

Accurate computation of the thickness of the articular cartilage is of great importance when diagnosing and monitoring the progress of joint diseases such as osteoarthritis. A fully automated cartilage assessment method is preferable compared to methods using manual interaction in order to avoid inter- and intra-observer variability. As a first step in the cartilage assessment, we present an automatic method for locating articular cartilage in knee MRI using supervised learning. The next step will be to fit a variable shape model to the cartilage, initiated at the location found using the method presented in this paper. From the model, disease markers will be extracted for the quantitative evaluation of the cartilage. The cartilage is located using an ANN-classifier, where every voxel is classified as cartilage or non-cartilage based on prior knowledge of the cartilage structure. The classifier is tested using leave-one-out-evaluation, and we found the average sensitivity and specificity to be 91.0% and 99.4%, respectively. The center of mass calculated from voxels classified as cartilage are similar to the corresponding values calculated from manual segmentations, which confirms that this method can find a good initial position for a shape model.