Our method was tested on a dataset consisting of 14 volumetric 3T PD VISTA coronal knee joint MRI scans of osteoarthritis (OA) patients. All images were acquired using Achieva 3.0T Philips Medical systems with a pixel size of 0.31mm, a slice thickness of 0.5mm and 512 x 512 resolutions. For training atlases, all images were manually segmented by three experts and tested by leave-one-out validation method. In atlas selection, coronal slab-average projection images were generated in training and target image and 2D affine registration was performed to select similar training images to the target image. In bone segmentation, bone atlases were aligned to the target bone by volume- and object-based two-stage 3D affine registration. Bone was then segmented with these aligned bone atlases using LWV and graph cuts. In cartilage segmentation, cartilage atlases were aligned to the target cartilage by applying bone's 3D affine transformations. Then shape-constrained weighting of LWV and graph cuts were proposed to restrict the cartilage label near the bone surfaces. For evaluation, our segmentation results were visually assessed and Dice similarity coefficient was measured between automatically and manually segmented femur and femoral cartilages.