The pythagorean averages as group images in efficient groupwise registration

Many applications in medical image processing can benefit from robust and unbiased groupwise registration. However, no obvious solution is available for multimodal registration problems involving a large number of images. A technique that is frequently applied calculates the sum of the pairwise similarities between a group image and all the images in the group. This allows the algorithm to scale linearly with respect to the number of images involved. Typically the arithmetic average is used as the group image, which has been shown to be a poor choice. We present geometric and harmonic averaging as an alternative and validate their performance in monoand multimodal experiments. These show an increased robustness and accuracy compared to the arithmetic average.

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