Spatially-varying intensity noise is a common source of distortion in medical images and is often associated with reduced accuracy in medical image registration. In this paper, we propose two multi-resolution image registration algorithms based on Empirical Mode Decomposition (EMD) that are robust against additive spatially-varying noise. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. Our first proposed algorithm (LR-EMD) is based on the registration of EMD feature maps from both floating and reference images in various resolutions. In the second algorithm (AFR-EMD), we first extract a single average feature map based on EMD and then use a simple hierarchical multi-resolution algorithm to register the average feature maps. We then showcase the superior performance of both algorithms in the registration of brain MRIs as well as retina images. For the registration of brain MR images, using mutual information as the similarity measure, both AFR-EMD and LR-EMD achieve a lower error rate in intensity (42% and 32%, respectively) and lower error rate in transformation (52% and 41%, respectively) compared to intensity-based hierarchical registration. Our results suggest that the two proposed algorithms offer robust registration solutions in the presence of spatially-varying noise.
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