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Spatially-varying intensity noise is a common source of distortion in medicalimages. Bias field noise is one example of such a distortion that is oftenpresent in the magnetic resonance (MR) images or other modalities such asretina images. In this paper, we first show that the bias field noise can beconsiderably reduced using Empirical Mode Decomposition (EMD) technique. EMD isa multi-resolution tool that decomposes a signal into several principlepatterns and residual components. We show that the spatially-varying noise ishighly expressed in the residual component of the EMD and could be filteredout. Then, we propose two hierarchical multi-resolution EMD-based algorithmsfor robust registration of images in the presence of spatially varying noise.One algorithm (LR-EMD) is based on registration of EMD feature-maps from bothfloating and reference images in various resolution levels. In the secondalgorithm (AFR-EMD), we first extract an average feature-map based on EMD fromboth floating and reference images. Then, we use a simple hierarchicalmulti-resolution algorithm to register the average feature-maps. For the brainMR images, both algorithms achieve lower error rate and higher convergencepercentage compared to the intensity-based hierarchical registration.Specifically, using mutual information as the similarity measure, AFR-EMDachieves 42% lower error rate in intensity and 52% lower error rate intransformation compared to intensity-based hierarchical registration. ForLR-EMD, the error rate is 32% lower for the intensity and 41% lower for thetransformation. Furthermore, we demonstrate that our proposed algorithmsimprove the registration of retina images in the presence of spatially varyingnoise.

Robust registration of medical images in the presence of spatially-varying noise