Medical image alignment based on landmark- and approximate contour-matching

Purpose: Our goal is to propose a landmark- and contour-matching (LCM) registration method that uses both landmark information and approximate point correspondences to boost the similarity between image pairs with sparse landmark information. Approach: A model for registering two-dimensional (2D) medical images with landmark information and contour-approximating landmarks was proposed. The model was also extended to accommodate the registration of three-dimensional (3D) cardiac images. We validated the LCM method on 2D hand x-rays and 3D porcine cardiac magnetic resonance images. The following metrics were used to assess the quality of specific aspects of the registered images: Dice similarity coefficient for the overall image overlap, target registration error for pointwise correspondence, and interior angle for local curvature. Results: Target registrations were reduced from 27.12 to 0.01 mm post-LCM registration. Implementing the proposed algorithm also led to a 112% average improvement in image similarity in terms of Dice coefficients. In addition, interior angle measurements indicate that the proposed method preserved the local curvature at major reference landmarks and mitigated the appearance of deformities in the registered images. Conclusions: The proposed method addressed several issues associated with purely landmark-based techniques, such as iterative closest point registration and thin plate spline interpolation. Furthermore, it provided accurate registration results even in the presence of landmark localization errors.