High-Dimensional Normalized Mutual Information for Image Registration Using Random Lines
Mutual information has been successfully used as an effective similarity measure for multimodal image registration. However, a drawback of the standard mutual information-based computation is that the joint histogram is only calculated from the correspondence between individual voxels in the two images. In this paper, the normalized mutual information measure is extended to consider the correspondence between voxel blocks in multimodal rigid registration. The ambiguity and high-dimensionality that appears when dealing with the voxel neighborhood is solved using uniformly distributed random lines and reducing the number of bins of the images. Experimental results show a significant improvement with respect to the standard normalized mutual information.
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