Inter-point procrustes: identifying regional and large differences in 3D anatomical shapes.

This paper presents a new approach for the robust alignment and interpretation of 3D anatomical structures with large and localized shape differences. In such situations, existing techniques based on the well-known Procrustes analysis can be significantly affected due to the introduced non-Gaussian distribution of the residuals. In the proposed technique, influential points that induce large dissimilarities are identified and displaced with the aim to obtain an intermediate template with an improved distribution of the residuals. The key element of the algorithm is the use of pose invariant shape variables to robustly guide both the influential point detection and displacement steps. The intermediate template is then used as the basis for the estimation of the final pose parameters between the source and destination shapes, enabling to effectively highlight the regional differences of interest. The validation using synthetic and real datasets of different morphologies demonstrates robustness up-to 50% regional differences and potential for shape classification.