Scan-Based Volume Animation Driven by Locally Adaptive Articulated Registrations

Abstract

Abstract-This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the non-linear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.

Combining variational and model-based techniques to register PET and MR images in hand osteoarthritis

“…Martin-Fernandez et al (2005, 2009) also used a piecewise approach based on a wire model (with an interpolation function) and manually specified landmarks for 2D registration of radiographs of hands. Rhee et al (2010) manually define phalange regions in a template MR volume and register by minimizing sum of squared differences for each phalange (with manual initialization) to recover hand motion in MR sequences.…”