Novel binning method for improved accuracy and speed of volume image coregistration using normalized mutual information

There is a growing consensus that mutual voxel information based measures hold great promise for fully automated multimodal image registration. We have found that image greyscale binning using a specific variation of contrast-limited histogram equalization (which we call histogram preservation) provides significant reduction of noise and spurious local maxima in the normalized mutual information function without musing significant displacement or smoothing of the global maximum. These effects are also relatively robust in the presence of image subsampling, so that accurate subpixel coregistration of typical medical volume images may be achieved in a few seconds by a very simple optima search algorithm based on a few thousand sampled voxels. In this aim, we illustrate these effects by presenting the results of random tests on patient data. Intramodal performance is evaluated by image self-reregistration using a variety of patient image volumes. Reregistration error is measured as the mean of the residual Euclidean displacement of the eight corner points of the image volumes after reregistration. The performance of histogram preservation prebinning is compared to linear prebinning, and the effect of image subsampling and number of bins on algorithm speed and accuracy is also assessed.