In addition to seeking geometric correspondence between the inputs, a legitimate image registration algorithm should also
keep the estimated transformation meaningful or regular. In this paper, we present a mathematically sound formulation that
explicitly controls the deformation to keep each grid in a meaningful shape over the entire geometric matching procedure.
The deformation regularity conditions are enforced by maintaining all the moving neighbors as non-twist grids. In contrast
to similar works, our model differentiates and formulates the convex and concave update cases under an efficient and
straightforward point-line/surface orientation framework, and uses equality constraints to guarantee grid regularity and
prevent folding. Experiments on MR images are presented to show the improvements made by our model over the popular
Demon's and DCT-based registration algorithms.
Registration and segmentation are two most important problems in the
field of medical image analysis. Traditionally, they were treated as
separate problems. In this paper, we introduce a unified variational framework for simultaneously carrying out image segmentation and registration. Segmentation information is integrated into the process of registration in leading to a more stable and noise-tolerant shape evolution, while a diffusion model is used to infer the volumetric deformation across the image. One of the major advantages of our model is its robustness against image noise. We present several 2D examples on synthetic and real data.
Abstract Mutual information (MI) is currently the most popular match metric in handling the registration problem for multi modality images. However, interpolation artifacts impose deteriorating effects to the accuracy and robustness of MI-based methods. This paper analyzes the generation mechanism of the artifacts inherent
in linear partial volume interpolation (PVI) and shows that the mutual information resulted from PVI is a convex function within each voxel grid. We conclude that the generation of the artifacts is due to two facts: 1) linear interpolation causes the histogram bin values to change at a synchronized pace; 2) entropy computation function Σxlgx is convex. As a remedy we propose to use non-uniform interpolation functions as the interpolation kernels in estimating the
joint histogram. Cubic B-splin and Gaussian interpolators are compared and we demonstrate the improvements via experiments on misalignments between CT/MR brain scans.