Surface-based 3D image registration using the iterative closest-point algorithm with a closest-point transform

Image registration is a valuable technique for medical diagnosis and treatment. It allows physicians to combine information from multiple images by aligning them into the same coordinate space. Surface-based methods register images by aligning corresponding surfaces of one or more anatomical structures such as the surface of the brain or skull. In this paper we examine a novel implementation of this approach. We use the iterative closest point algorithm to iteratively search for the transformation that minimizes the distance between surface points in one image and a surface model in the other image. In each iteration we use the closest point transform to find corresponding points that are closest with respect to the transformation estimated in the previous iteration. Results from several experiments are presented to demonstrate the efficacy of this approach.