Curvature-driven diffusion-based mathematical image registration models

Curvature-based Image Registration: Review and Extensions

Contourlet-Based Edge Extraction for Image Registration

Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally image resampling. As the accuracy of a registration process is highly dependent to the fe...

Uncertainty-Driven, Point-Based Image Registration

Point-based registration is the problem of computing the transformation that best aligns two point sets, such as might be obtained using range scanners or produced by feature extraction algorithms. The Iterative Closest Points (ICP) algorithm and its variants are the most commonly used techniques for point-based registration. The ICP algorithm may be derived as the solution to a global optimiza...

Remeshing by Curvature Driven Diffusion

We present a method to regularize an arbitrary topology mesh M, which defines a piecewise linear approximation of a surface M , with the purpose of having an accurate representation of M : the density of the nodes should correlate with the regularity of M . We use the mean curvature as an intrinsic measure of regularity. Unlike sophisticated parameterization-dependent techniques, our parameteri...

Diffusion-tensor image registration

In this chapter, we introduce the problem of registering diffusion tensor magnetic resonance (DT-MR) images. The registration task for these images is made challenging by the orientational information they contain, which is affected by the registration transformation. This information about orientation and other aspects of the diffusion tensor are exploited in the development of similarity meas...