3-D methods for difference estimation in volumetric data

The estimation of the frame or slice difference in volumetric data is an important task in applications such as tracking, registration and segmentation. In this paper, we consider the problem of difference estimation in multi-texture data defined on a 3-D lattice. Each texture is modelled via a stationary Gaussian Markov random field (GMRF). Two block-matching methods are proposed for the difference field estimation. The first method uses a 3-D cross-correlation coefficient as a similarity measure. The second method is based on minimization of the Kullback-Leihler distance between the conditional class probability mass functions (p.m.f.s) of the blocks to be matched. The performance of the methods is tested on synthetic 3-D textures and on real MRI images. Difference-compensated supervised segmentation is shown to be an important application context.