Hierarchical vs. simultaneous multiresolution strategies for nonrigid image registration

Nonrigid image registration algorithms commonly employ multiresolution strategies, both for the image and the transformation model. Usually a hierarchical approach is chosen: the algorithm starts on a level with reduced complexity, e.g. a smoothed and downsampled version of the input images, and with a limited number of degrees of freedom for the transformation. Gradually the level of complexity is increased until the original, non-smoothed images are used, and the transformation model has the highest degrees of freedom. In this study, we define two alternative approaches in which low- and high-resolution levels are considered simultaneously. An extensive experimental comparison study is performed, evaluating all possible combinations of multiresolution schemes for image data and transformation model. Publicly available CT lung data, with annotated landmarks, are used to quantify registration accuracy. It is shown that simultaneous multiresolution strategies can lead to more accurate registration.

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