Optimal grid point selection for improved nonrigid medical image registration

Abstract

Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between medical images acquired from different individuals or atlases, among others. This type of registration defines a deformation field that gives a translation or mapping for every pixel in the image. One popular local approach for estimating this deformation field, known as block matching, is where a grid of control points are defined on an image and are each taken as the centre of a small window. These windows are then translated in the second image to maximise a local similarity criterion. This generates two corresponding sets of control points for the two images, yielding a sparse deformation field. This sparse field can then be propagated to the entire image using well known methods such as the thin-plate spline warp or simple Gaussian convolution. Previous block matching procedures all utilise uniformly distributed grid points. This results in the generation of a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. That is, results are better in regions of high information when compared to regions of low information. Consequently, this paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo (RJMCMC) statistical procedure to optimally select grid points of interest. These grid points have a greater concentration in regions of high information and a lower concentration in regions of small information. Results show that non-rigid registration can by improved by using optimally selected grid points of interest.