Robust Image Registration Using Mixtures of T-Distributions

Abstract

We propose a pixel similarity-based algorithm enabling accurate rigid registration between single and multimodal images presenting gross dissimilarities due to noise, missing data or outlying measures. The method relies on the partitioning of a reference image by a Student's t-mixture model (SMM). This partition is then projected onto the image to be registered. The main idea is that a t-component in the reference image corresponds to a t-component in the image to be registered. If the images are correctly registered the weighted sum of distances between the corresponding components is minimized. The use of SMM components is justified by the property that they have heavier tails than standard Gaussians, thus providing robustness to outliers. Experimental results indicate that, even in the case of images presenting low SNR or important amount of dissimilarities due to temporal changes, the proposed algorithm compares favorably to the histogram-based mutual information method that is widely used in a variety of applications.