A pyramid approach to subpixel registration based on intensity

TLDR

The algorithm automatically registers images or volumes by minimizing the mean‑square intensity difference between a reference and a test dataset. It employs a spline‑based representation of the data, a coarse‑to‑fine pyramid strategy, and a modified Marquardt‑Levenberg optimization of a global 3‑D affine (optionally rigid) model with optional contrast adjustment. The method yields excellent registration of intramodality PET and fMRI data, is more robust than single‑stage approaches, and runs faster than the standard Marquardt‑Levenberg algorithm.

Abstract

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.