Orthopedists invest significant amounts of effort and time trying to understand the biomechanics of articular joints and to quantify the differences between two datasets.
We present a framework that enables the cross-dataset visual exploration and analysis of articular joint biomechanics. Central to our approach is a computer-vision inspired markerless method for establishing a set of correspondences between subject-specific datasets. Manifold geometry-models are subsequently defined and deformed from one subject-specific dataset to another dataset such that the markerless correspondences are preserved while minimizing model distortion. The resulted manifold-correspondence and a variety of visualization techniques allow the users to explore the geometry and kinematics similarities and differences between datasets, and to define meaningful quantitative measures.
[manuscript in review]