title: Parameter space warping: shape-based correspondence between morphologically different objects.
abstract: This paper presents a novel and comprehensive method for the automated determination of correspondences between two morphologically different two-dimensional (2-D) or three-dimensional (3-D) objects. Correspondences are determined by warping parametric representations of the objects to be matched. The warp is guided by the minimization of a similarity criterion function that measures features related to structural correspondence, including Euclidian point-to-point distance and differences in normals and curvature. The method uses a continuous harmonic parameterization for both the object and the warp, which provides: 1) a high degree of computational efficiency; 2) robust extraction of differential features, not subject to discretization errors or noise amplification in differentiation; 3) direct formulation of constraints to avoid overlaps in the resulting correspondence set; and 4) a scale-space paradigm of object shape and warp. The new method does not search for individual landmarks, but operates with a complete, integrated representation of the object geometry. The method was tested on 2-D and 3-D objects with substantial shape differences. Results demonstrated substantial improvements of 2%-33% in correspondence accuracy and 15%-59% in correspondence quality compared with direct registration methods.
authors.affiliation: Whitaker Biomedical Imaging Laboratory, Department of Biomedical Engineering, Cleveland Clinic Foundation, OH 44195, USA. meier@bwh.harvard.edu
authors.forename: Dominik
authors.initials: D
authors.lastname: Meier
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Fisher
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000706:statistics & numerical data; D008953:Models, Anatomic; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity