Normal vector information registration and comparisons with mutual information

We propose a new similarity measure for medical image registration and make comparisons with two kinds of mutual information (MI) methods in the literature. This new measure is computed from the normal vector information (NVI) of the images instead of pixel intensity that the NU methods based. The NVI of an image is extracted from the relationship between pixels and computed from the normal vector of the pixel based on their local isosurface. The NVI method has been proved to be able to successfully register two-dimensional (2D) images. In this paper, we will apply it in three-dimensional (3D) medical images and employ the known-result datasets to quantitatively evaluate the performances of the NVI and Nit methods. The visual assessment is employed for the unknown-result clinical image dataset registrations. The results show that the NVI method is ready to be affected by the salt-and-pepper noise; while when the random noise is removed, the NVI method performs no worse than the MI methods.