Computational anatomy: an emerging discipline
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Landmark-based thin-plate spline image registration is one of the most commonly used methods for non-rigid medical image registration and anatomical shape analysis. It is well known that this method does not produce a unique correspondence between two images away from the landmark locations because interchanging the role of source and target landmarks does not produce forward and reverse transformations that are inverses of each other. In this paper, we present two new image registration algorithms that minimize the thin-plate spline bending energy and the inverse consistency error--the error between the forward and the inverse of the reverse transformation. The landmarkbased consistent thin-plate spline algorithm registers images given a set of corresponding landmarks while the intensity-based consistent thinplate spline algorithm uses both corresponding landmarks and image intensities. Results are presented that demonstrate that using landmark and intensity information to jointly estimate the forward and reverse transformations provides better correspondence than using landmarks or intensity alone.