The purpose of this study is to develop a three-dimensional registration method for monitoring knee joint disease from magnetic resonance (MR) image data sets. A global optimization technique was used for identifying anatomically corresponding points of knee femur surfaces (bone cartilage interfaces). In a first pre-registration step, we used the principal axes transformation to correct for different knee joint positions and orientations in the MR scanner. In a second step, we presented a global search algorithm based on Lipschitz optimization theory. This technique can simultaneously determine the translation and rotation parameters through searching a six-dimensional space of Euclidean motion metrics (translation and rotation) after calculating the point correspondences. The point correspondences were calculated by using the Hungarian algorithm. The accuracy of registration was evaluated using 20 porcine knees. There were 300 corresponding landmark points over the 20 pig knees. We evaluated the registration accuracy by measuring the root-mean-square distance (RMSD) error of corresponding landmark points between two femur surfaces (two time-points). The results show that the average RMSD was 1.22 ± 0.10 mm (SD) by the iterative closest point (ICP) method, 1.17 ± 0.10 mm the by expectation-maximization-ICP method, 1.02 ± 0.06 mm by the genetic method, and 0.93 ± 0.04 mm by the proposed method. Compared with the other three registration approaches, the proposed method achieved the highest registration accuracy.