Purpose: Reconstructive surgeries to treat a number of musculoskeletal conditions, from arthritis to severe trauma, involve implant placement and reconstructive planning components. Anatomically matched 3D printed implants are becoming increasingly patient-specific; however the preoperative planning and design process requires several hours of manual effort from highly trained engineers and clinicians. Our work mitigates this problem by proposing algorithms for the automatic re-alignment of unhealthy anatomies, leading to more efficient, affordable, and scalable treatment solutions. Methods: Our solution combines global alignment techniques such as iterative closest points (ICP) with novel joint space refinement algorithms. The latter is achieved by a low dimensional characterization of the joint space, computed from the distribution of the distance between adjacent points in a joint. Results: Experimental validation is presented on real clinical data from human subjects. Compared with ground truth healthy anatomies, our algorithms can reduce misalignment errors by 22% in translation and 19% in rotation for the full foot-and-ankle and 37% in translation and 39% in rotation for the hind-foot only, achieving a performance comparable to expert technicians. Conclusion: Our methods and histogram-based metric allow for automatic and unsupervised alignment of anatomies, a major step toward a fully automated and data driven re-positioning, designing, and diagnosing tool.